{"query": "Please generate the Abstract section for this paper.", "gt": "Conventional out-of-distribution (OOD) detection schemes based on variational autoencoder or Random Network Distillation (RND) have been observed to assign lower uncertainty to the OOD than the target distribution. In this work, we discover that such conventional novelty detection schemes are also vulnerable to the blurred images. Based on the observation, we construct a novel RND-based OOD detector, SVD-RND, that utilizes blurred images during training. Our detector is simple, efficient at test time, and outperforms baseline OOD detectors in various domains. Further results show that SVD-RND learns better target distribution representation than the baseline RND algorithm. Finally, SVD-RND combined with geometric transform achieves near-perfect detection accuracy on the CelebA dataset.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 109 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper proposes a new method for detecting out-of-distribution (OOD) data, called SVD-RND, which uses blurred data as adversarial examples to improve OOD detection performance. The method is trained using the idea of Random Network Distillation (RND) and discriminates the training data from their blurred versions. The proposed method is evaluated in challenging scenarios where conventional generative models show nearly 50% detection accuracy. The results show that SVD-RND outperforms conventional baselines and shows improvements on domains where conventional OOD detection schemes show moderate results. The method is also compared to other blurring techniques and the results show that SVD-RND is the most effective method for OOD detection.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Generative adversarial nets , Authors: Ian J. Goodfellow Jean Pouget-Abadie... , 2014\n2.  Title: Explaining and harnessing adversarial examples , Authors: Ian J. Goodfellow Jonathon Shlens... , 2014\n3.  Title: Do deep generative models know what they don\u2019t know , Authors: Eric Nalisnick Akihiro Matsukawa... , 2019\n4.  Title: Detecting out-of-distribution inputs to deep generative models using a test for typicality , Authors: Eric Nalisnick Akihiro Matsukawa... , 2019\n5.  Title: Generative probabilistic novelty detection with adversarial autoencoders , Authors: Stanislav Pidhorskyi Ranya Almohsen... , 2018\n6.  Title: Deep one-class classification , Authors: Lukas Ruff Robert Vandermeulen... , 2018\n7.  Title: Deep semi-supervised anomaly detection , Authors: Lukas Ruff Robert Vandermeulen... , 1906\n8.  Title: Adversarially learned one-class classifier for novelty detection , Authors: Mohammad Sabokrou Mohammad Khalooei... , 2018\n9.  Title: Efficient GAN-based anomaly detection , Authors: Houssam Zenati Chuan Sheng Foo... , 2018\n", "gt": "The goal of OOD detection is to determine whether the data is sampled from the target distribution D. Therefore, based on the training data Dtrain \u0102 D, we train a scalar function that expresses the\nconfidence, or uncertainty of the data. The performance of the OOD detector is tested on the Dtest \u0102 D against the OOD dataset DOOD. We denote an in-distribution data and OOD pair as In : Out in this paper, e.g., CIFAR-10 : SVHN. In this section, we mention only closely related work to our research. For a broader survey on deep OOD detection, we recommend the paper from Chalapathy et al. (2019). OOD Detection: Majority of OOD detection methods rely on a reconstruction error and representation learning. (Ruff et al., 2018) trained a deep neural network to map data into a minimum volume hypersphere. Generative probabilistic novelty detection (GPND) (Pidhorskyi et al., 2018) employed the distance to the latent data manifold as the confidence measure and trained the adversarial autoencoder (AAE) to model the manifold. Deep generative models are widely employed for latent space modeling in OOD detection (Zenati et al., 2018; Sabokrou et al., 2018). However, a recent paper by Nalisnick et al. (2019) discovered that popular deep generative models, such as variational autoencoder (VAE) (Kingma et al., 2014) or GLOW (Kingma et al., 2018), fail to detect simple OOD. While adversarially trained generative models, such as generative adversarial networks (GAN) (Goodfellow et al., 2014) or AAE, are not discussed in Nalisnick et al. (2019), our experiments with GPND show that such models can also struggle to detect such simple OODs. OOD Detection with Additional Data: Some methods try to solve OOD detection by appending additional data or labels for training.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 287 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper proposes a new method for detecting out-of-distribution (OOD) data, which is the task of distinguishing samples from unseen distribution from the training distribution. The proposed method, SVD-RND, uses blurred data as adversarial examples to train a predictor network to discriminate between original and blurred datasets. The method shows significant performance gains compared to conventional baselines in challenging scenarios where vanilla generative models show only 50% detection accuracy. The authors also compare the performance of SVD-RND to other methods such as RND, GPND, geometric transforms, and typicality test. The results show that SVD-RND outperforms all other methods in every metric. The paper also discusses the representation learning capabilities of SVD-RND and proposes a new heuristic for training SVD-RND in scenarios where OOD validation data is not available.", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "Summary: They tackle the problem of out-of-data distribution by leveraging RND applied to data augmentations. They train a model f(x) to match the outputs of g_i(aug_i(x)), where g_i is a random network and aug_i is a particular type of augmentation. An example with high error in this task is treated as an out-of-distribution example. This work focuses on exploring blurring through SVD, where the smallest K singular values are set to 0, and K varies between different aug_i calls. They find that their method of consistently can achieve strong detection rates across multiple target-dataset pairs.\n\nComments:\n* The experimental results in this work are impressive, which introduces many more questions.\n* The model used for f and g is not mentioned in the text.\n* Figure 4 (left) suggests that the SVD-RND performs about the same between 10K and 50K examples. The level of robustness is surprising, but doesn\u2019t seem to square with intuition that more data ought to help. How little data can be used? In other words, extend the graph to the left. \n* The geometric transforms baseline is not fair, since SVD-RND uses multiple SVD transforms (b_train > 1) whereas the geometric transforms only have one. Please run a model with all the geometric transforms. This result is important for understanding whether the gains come from the particular transform (SVD) or the number of transforms used.\n* Following the spirit of the previous comment, what other data augmentations can be used in place of SVD? Typical image classification pipelines use a large variety of augmentations. I would suggest taking some augmentations from AutoAugment [1] and running RND on top of them.\n* An experiment that is missing is RND trained on blurred images. Is the blurring itself the important component, or is having multiple different heads important?\n* In general, I am confused about how a single head RND does not converge to 0 loss by learning the weights of g. This seems to be a simple optimization problem. The original RND paper avoided this problem by also using the network to learn a policy, but this does not exist in this approach.\n\nIn general, this work seems promising, but lacks proper ablations that elucidate what components of the method are important. I am happy to increase my score if the experiments suggests are added to the work.\n\n6: Weak Accept", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 394 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper proposes a new method for out-of-distribution (OOD) detection called SVD-RND, which uses blurred data as adversarial examples to improve OOD detection performance. The authors found that conventional OOD detection methods assign higher likelihood to blurred data than to original data, which motivated them to employ blurring to prevent the OOD detector from overfitting to low resolution. SVD-RND is trained using the idea of Random Network Distillation (RND) to discriminate the training data from their blurred versions. The proposed method shows significant performance gains over conventional baselines in challenging scenarios, such as CIFAR-10 vs. SVHN and ImageNet vs. CIFAR-10. The authors also compare SVD-RND to other blurring techniques and find that it outperforms them.\n\nOverall, the paper presents a novel approach to OOD detection that uses blurred data as adversarial examples to improve detection performance. The method is evaluated on several challenging scenarios and shows significant improvements over conventional baselines. The use of blurred data as adversarial examples is an interesting idea that could potentially be applied to other OOD detection methods.\n\nOne potential concern with the proposed method is that it relies on the assumption that blurred data is more representative of OOD data than original data. It would be interesting to see how the method performs on data that is not blurred but is still OOD. Additionally, it would be useful to see how the method performs on larger datasets and in more diverse settings.\n\nAnother question that arises is how the method can be applied in practice. While the authors provide a detailed description of the method and its implementation, it would be helpful to have more information on how to use the method in a real-world setting. For example, how can the method be integrated into an existing OOD detection pipeline, and what are the potential limitations and trade-offs of using SVD-RND in practice?\n\nOverall, the paper presents an interesting approach to OOD detection that could potentially improve detection performance on challenging scenarios. However, further evaluation and practical considerations are needed to fully assess the effectiveness and practicality of the proposed method.", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Self-supervised learning is showing great promise for monocular depth estimation, using geometry as the only source of supervision. Depth networks are indeed capable of learning representations that relate visual appearance to 3D properties by implicitly leveraging category-level patterns. In this work we investigate how to leverage more directly this semantic structure to guide geometric representation learning, while remaining in the self-supervised regime. Instead of using semantic labels and proxy losses in a multi-task approach, we propose a new architecture leveraging fixed pretrained semantic segmentation networks to guide self-supervised representation learning via pixel-adaptive convolutions. Furthermore, we propose a two-stage training process to overcome a common semantic bias on dynamic objects via resampling. Our method improves upon the state of the art for self-supervised monocular depth prediction over all pixels, fine-grained details, and per semantic categories.\u2020", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 134 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper presents a novel architecture for self-supervised monocular depth estimation that leverages semantic information from a fixed pretrained network to guide the generation of multi-level depth features via pixel-adaptive convolutions. The proposed method improves the state-of-the-art in self-supervised monocular depth estimation on the standard KITTI benchmark, both on average over pixels, over classes, and for dynamic categories in particular. The authors also introduce a two-stage training process that addresses the common bias on dynamic objects resulting in predicting them at infinite depths. The proposed architecture is general and can be applied to different depth networks. The results show that the introduction of semantically-guided geometric representation learning further improves upon the current state of the art in self-supervised monocular depth estimation.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Exploiting semantic information and deep matching for optical flow , Authors: Min Bai Wenjie Luo... , 2016\n2.  Title: Depth prediction without the sensors: Leveraging structure for unsupervised learning from monocular videos , Authors: Vincent Casser Soeren Pirk... , 2019\n3.  Title: Depth map prediction from a single image using a multi-scale deep network , Authors: David Eigen Christian Puhrsch... , 2014\n4.  Title: Deep ordinal regression network for monocular depth estimation , Authors: Huan Fu Mingming Gong... , 2018\n5.  Title: Unsupervised cnn for single view depth estimation: Geometry to the rescue , Authors: Ravi Garg Vijay Kumar BG... , 2016\n6.  Title: Unsupervised monocular depth estimation with left-right consistency , Authors: Cl\u00e9ment Godard Oisin Mac Aodha... , 2017\n7.  Title: Digging into selfsupervised monocular depth prediction , Authors: Cl\u00e9ment Godard Oisin Mac Aodha... , 2018\n8.  Title: Packnet-sfm: 3d packing for self-supervised monocular depth estimation , Authors: Vitor Guizilini Sudeep Pillai... , 2019\n9.  Title: Spatial transformer networks. In Advances in neural information processing , Authors: Max Jaderberg Karen Simonyan... , 2017\n10.  Title: Single-image depth estimation based on fourier domain analysis , Authors: Jae-Han Lee Minhyeok Heo... , 2018\n11.  Title: Spigan: Privileged adversarial learning from simulation , Authors: Kuan-Hui Lee German Ros... , 2019\n12.  Title: Depth and surface normal estimation from monocular images using regression on deep features and hierarchical crfs , Authors: Bo Li Chunhua Shen... , 2015\n13.  Title: Learning to fuse things and stuff , Authors: Jie Li Allan Raventos... , 2018\n14.  Title: Unsupervised learning of depth and egomotion from monocular video using 3d geometric constraints , Authors: Reza Mahjourian Martin Wicke... , 2018\n15.  Title: Geonet: Geometric neural network for joint depth and surface normal estimation , Authors: Xiaojuan Qi Renjie Liao... , 2018\n16.  Title: Demon: Depth and motion network for learning monocular stereo , Authors: Benjamin Ummenhofer Huizhong Zhou... , 2017\n17.  Title: Learning depth from monocular videos using direct methods , Authors: Chaoyang Wang Jos\u00e9 Miguel Buenaposada... , 2018\n18.  Title: Image quality assessment: from error visibility to structural similarity , Authors: Zhou Wang Alan C Bovik... , 2004\n19.  Title: Deep virtual stereo odometry: Leveraging deep depth prediction for monocular direct sparse odometry , Authors: Nan Yang Rui Wang... , 2018\n20.  Title: Unsupervised learning of geometry with edge-aware depth-normal consistency , Authors: Zhenheng Yang Peng Wang... , 2017\n", "gt": "Since the seminal work of Eigen et al. (2014), substantial progress has been done to improve the accuracy of supervised depth estimation from monocular images, including the use of Conditional Random Fields (CRFs) (Li et al., 2015), joint optimization of surface normals (Qi et al., 2018), fusion of multiple depth maps (Lee et al., 2018), and ordinal classification (Fu et al., 2018). Consequently, as supervised techniques for depth estimation advanced rapidly, the availability of largescale depth labels became a bottleneck, especially for outdoor applications. Garg et al. (2016) and Godard et al. (2017) provided an alternative self-supervised strategy involving stereo cameras, where Spatial Transformer Networks (Jaderberg et al., 2015) can be used to geometrically warp, in a differentiable way, the right image into a synthesized left image, using the predicted depth from the left image. The photometric consistency loss between the resulting synthesized and original left images can then be minimized in an end-to-end manner using a Structural Similarity term (Wang et al., 2004) and additional depth regularization terms. Following Godard et al. (2017) and Ummenhofer et al. (2017), Zhou et al. (2017) generalized this to the purely monocular setting, where a depth and a pose networks are simultaneously learned from unlabeled monocular videos. Rapid progress in terms of architectures and objective functions (Yin & Shi, 2018; Mahjourian et al., 2018; Casser et al., 2019; Zou et al., 2018; Klodt & Vedaldi, 2018; Wang et al., 2018; Yang et al., 2018) have since then turned monocular depth estimation into one of the most successful applications of self-supervised learning, even outperforming supervised methods (Guizilini et al., 2019). The introduction of semantic information to improve depth estimates has been explored in prior works, and can be broadly divided into two categories. The first one uses semantic (or instance) information to mask out or properly model dynamic portions of the image, which are not accounted for in the photometric loss calculation. Gu\u0308ney & Geiger (2015) leveraged object knowledge in a Markov Random Field (MRF) to resolve stereo ambiguities, while Bai et al.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 339 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper proposes a novel architecture for self-supervised monocular depth estimation that leverages semantic information from a fixed pretrained network to guide the generation of multi-level depth features via pixel-adaptive convolutions. The proposed architecture is composed of two networks: a primary one responsible for the generation of depth predictions and a secondary one capable of producing semantic predictions. The primary network is optimized during self-supervised learning, while the secondary network is initialized from pretrained weights and is not further optimized. The proposed approach differs from the common practice of supervised (ImageNet) pretraining of depth encoders by leveraging fixed intermediate feature representations as a way to maintain consistent semantic guidance throughout training. The authors also propose a two-stage training process to address the infinite depth problem, which is caused by the presence of objects moving at the same speed as the camera. The first stage is used to learn a standard depth network on all available training data, and the second stage is used to resample the dataset by automatically filtering out sequences with infinite depth predictions that violate a basic geometric prior. The proposed approach is evaluated on the standard KITTI benchmark and is shown to outperform other methods that leverage semantic information by a substantial margin.", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "The paper proposes a using pixel-adaptive convolutions to leverage semantic labels in self-supervised monocular depth estimation. The semantic features are predicted by a pretrained network rather than relying on a ground truth. Moreover, a two-stage training process in proposed in order to filter out images leading to erroneous SfM predictions. The method is evaluated with different networks on the KITTY dataset.\n\nThe paper is very well written and clear. The applications of per-pixel convolutions to this problem seems sound and the experimental validation seems satisfactory. I have however one main concern (1) and a few additional questions below:\n\n1) While (Guizilini 2019) shows that using a larger set of unannotated videos and allows the self-supervised method to eventually outperform supervised methods, this study is not done here. This makes me question the applicability of the approach, as using large unlabelled videos would probably lead to noisy segmentations that could be unhelpful to the depth estimation. Showing an improvement over the supervised baseline would be a much stronger experimental validation, as for now it is difficult to know exactly why in which scenario this method should be used, rather than a supervised network or vanilla packnet.\n\n2) I see that you obtain the same numbers in Table 2 / PackNet / row 1 as in (Guizilini 2019); I would like to confirm that you used exactly their self-objective loss, in all your experiments? I would suggest adding to section 3.1. the fact the fact that the loss is the same is in (Guizlini 2019), as a reader could assume that there is novelty in the loss formulation.\n\n3) Have you tried fine-tuning the whole architecture including the semantic network end-to-end?\n\n6: Weak Accept", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 282 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper presents a novel architecture for self-supervised monocular depth estimation that leverages semantic information from a fixed pretrained network to guide the generation of multi-level depth features via pixel-adaptive convolutions. The proposed architecture is composed of two networks: a primary one responsible for the generation of depth predictions and a secondary one capable of producing semantic predictions. The primary network is optimized during self-supervised learning, while the secondary network is initialized from pretrained weights and is not further optimized. The proposed approach is evaluated on the standard KITTI benchmark, showing state-of-the-art performance in self-supervised monocular depth estimation. The paper also presents a two-stage training process to address the infinite depth problem in dynamic objects. Overall, the proposed architecture is a significant improvement over previous methods in self-supervised monocular depth estimation.\n\nIn terms of suggestions, it would be interesting to see how the proposed architecture performs on other datasets and how it compares to other state-of-the-art methods. Additionally, it would be useful to have a more detailed analysis of the impact of the two-stage training process on the performance of the model.\n\nIn terms of questions, it would be interesting to know how the proposed architecture compares to other methods that leverage semantic information, such as those that use ground-truth semantic segmentation and depth labels during training. Additionally, it would be useful to have a more detailed analysis of the impact of the two-stage training process on the performance of the model.", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "We analyze speed of convergence to global optimum for gradient descent training a deep linear neural network (parameterized as x 7\u2192 WNWN\u22121 \u00b7 \u00b7 \u00b7W1x) by minimizing the `2 loss over whitened data. Convergence at a linear rate is guaranteed when the following hold: (i) dimensions of hidden layers are at least the minimum of the input and output dimensions; (ii) weight matrices at initialization are approximately balanced; and (iii) the initial loss is smaller than the loss of any rank-deficient solution. The assumptions on initialization (conditions (ii) and (iii)) are necessary, in the sense that violating any one of them may lead to convergence failure. Moreover, in the important case of output dimension 1, i.e. scalar regression, they are met, and thus convergence to global optimum holds, with constant probability under a random initialization scheme. Our results significantly extend previous analyses, e.g., of deep linear residual networks (Bartlett et al., 2018).", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 152 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper presents a study on the landscape of deep learning optimization. It discusses the difficulties in understanding the properties of the landscape and the challenges in proving convergence to global minimum. The authors propose a trajectory-based approach and analyze the linear neural networks. They show that gradient descent converges to global minimum for linear neural networks with sufficient conditions. The paper also presents a new path to circumvent the obstacle of landscape limitations. The authors analyze the trajectory-based approach and show that it is beneficial for gradient descent. The paper concludes with a summary of the main findings and future directions for research.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Neural networks and principal component analysis: Learning from examples without local minima , Authors: Pierre Baldi Kurt Hornik , 1989\n2.  Title: The loss surfaces of multilayer networks , Authors: Anna Choromanska Mikael Henaff... , 2015\n3.  Title: On the power of over-parametrization in neural networks with quadratic activation , Authors: Simon S Du Jason D Lee , 2018\n4.  Title: Algorithmic regularization in learning deep homogeneous models: Layers are automatically balanced , Authors: Simon S Du Wei Hu... , 2018\n5.  Title: When is a convolutional filter easy to learn , Authors: Simon S Du Jason D Lee... , 2018\n6.  Title: Gradient descent learns onehidden-layer cnn: Dont be afraid of spurious local minima , Authors: Simon S Du Jason D Lee... , 2018\n7.  Title: Escaping from saddle pointsonline stochastic gradient for tensor decomposition , Authors: Rong Ge Furong Huang... , 2015\n8.  Title: Matrix completion has no spurious local minimum , Authors: Rong Ge Jason D Lee... , 2016\n9.  Title: Global optimality in neural network training , Authors: Benjamin Haeffele Ren\u00e9 Vidal , 2017\n10.  Title: Identity matters in deep learning , Authors: Moritz Hardt Tengyu Ma , 2016\n11.  Title: Deep learning without poor local minima , Authors: Kenji Kawaguchi , 2016\n12.  Title: Deep linear networks with arbitrary loss: All local minima are global , Authors: Thomas Laurent James Brecht , 2018\n13.  Title: Gradient descent only converges to minimizers , Authors: Jason D Lee Max Simchowitz... , 2016\n14.  Title: The loss surface of deep and wide neural networks , Authors: Quynh Nguyen Matthias Hein , 2017\n15.  Title: The loss surface and expressivity of deep convolutional neural networks , Authors: Quynh Nguyen Matthias Hein , 2018\n16.  Title: Gradient descent only converges to minimizers: Non-isolated critical points and invariant regions , Authors: Ioannis Panageas Georgios Piliouras , 2017\n17.  Title: Spurious local minima are common in two-layer relu neural networks , Authors: Itay Safran Ohad Shamir , 2018\n18.  Title: Exponential convergence time of gradient descent for one-dimensional deep linear neural networks , Authors: Ohad Shamir , 2018\n19.  Title: No bad local minima: Data independent training error guarantees for multilayer neural networks , Authors: Daniel Soudry Yair Carmon , 2016\n", "gt": "Theoretical study of gradient-based optimization in deep learning is a highly active area of research. As discussed in Section 1, a popular approach is to show that the objective landscape admits the properties of no poor local minima and strict saddle, which, by Ge et al. (2015); Lee et al. (2016); Panageas and Piliouras (2017), ensure convergence to global minimum. Many works, both classic (e.g. Baldi and Hornik (1989)) and recent (e.g. Choromanska et al. (2015); Kawaguchi (2016); Hardt and Ma (2016); Soudry and Carmon (2016); Haeffele and Vidal (2017); Nguyen and Hein (2017); Safran and Shamir (2018); Nguyen and Hein (2018); Laurent and Brecht (2018)), have focused on the validity of these properties in different deep learning settings. Nonetheless, to our knowledge,\n9https://github.com/tensorflow/tensorflow/tree/master/tensorflow/ examples/tutorials/mnist\n10As opposed to the dataset used in our experiments with linear networks, measuring the training loss with MNIST is non-trivial computationally (involves passing through 60K examples). Therefore, rather than continuously polling training loss until it reaches a certain threshold, in this experiment we chose to evaluate speed of convergence by measuring the training loss once after a predetermined number of iterations. the success of landscape-driven analyses in formally proving convergence to global minimum for a gradient-based algorithm, has thus far been limited to shallow (two layer) models only (e.g. Ge et al. (2016); Du and Lee (2018); Du et al. (2018a)). An alternative to the landscape approach is a direct analysis of the trajectories taken by the optimizer.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 243 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe Related Work section should discuss previous research on the topic of optimization of neural networks, particularly with regards to the landscape of the optimization problem and the existence of poor local minima. Some relevant papers to cite include \"Neural networks and principal component analysis: Learning from examples without local minima\" by Pierre Baldi and Kurt Hornik (1989), \"The loss surfaces of multilayer networks\" by Anna Choromanska, Mikael Henaff, et al. (2015), \"On the power of over-parametrization in neural networks with quadratic activation\" by Simon S. Du, Jason D. Lee, et al. (2018), \"Algorithmic regularization in learning deep homogeneous models: Layers are automatically balanced\" by Simon S. Du, Wei Hu, et al. (2018), \"When is a convolutional filter easy to learn\" by Simon S. Du, Jason D. Lee, et al. (2018), \"Gradient descent learns one-hidden-layer cnn: Don't be afraid of spurious local minima\" by Simon S. Du, Jason D. Lee, et al. (2018), \"Escaping from saddle points online stochastic gradient for tensor decomposition\" by Rong Ge, Furong Huang, et al. (2015), \"Matrix completion has no spurious local minimum\" by Rong Ge, Jason D. Lee, et al. (2016), \"Global optimality in neural network training\" by Benjamin Haeffele, Ren\u00e9 Vidal (2017), \"Identity matters in deep learning\" by Moritz Hardt, Tengyu Ma (2016), \"Deep learning without poor local minima\" by Kenji Kawaguchi (2016), \"Deep linear networks with arbitrary loss: All local minima are global\" by Thomas Laurent, James Brecht (2018), \"Gradient descent only converges to minimizers\" by Jason D. Lee, Max Simchowitz, et al. (2016), \"The loss surface of deep and wide neural networks\" by Quynh Nguyen, Matthias Hein (2017), \"The loss surface and expressivity of deep convolutional neural networks\" by Quynh Nguyen,", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "This paper studies the convergence of gradient descent on the squared loss of deep linear neural networks. The authors prove linear convergence rate if (1) the network dimensions are big enough so that the full product can have full rank, (2) the singular values of each weight matrices are approximately the same, (3) the initialized point is \u201cclose enough\u201d to the target.\n\nMy main concern about the study of GD on linear neural network is whether we really get any \u201cbenefit\u201d or \u201cacceleration\u201d from depth, i.e., is GD on linear neural nets any faster than GD on linear models. It\u2019s been shown that we get acceleration in some cases (e.g., $\\ell_p$ regression when $p>2$ [Arora et al. 18\u2019]), but some other results (e.g., [Shamir 18\u2019] mentioned in Section 5) show that GD on linear neural nets (when weight matrices are all scalar) suffer exponential (in depth) increase in convergence time at near zero region, due to the vanishing gradient phenomenon. From my understanding, this paper circumvents this problem by assuming deficiency margin, because in the setting of [Shamir 18\u2019], deficiency margin means that the initialized product ($W_{1:N}$) has the same sign as $\\Phi$ and far enough from zero, so we don\u2019t have to pass through the near-zero region.\n\nBelow, I\u2019ll list specific concerns/questions/comments.\n* In my opinion, the statements about \u201cnecessity\u201d of two key assumptions are too strong, because the authors only provide counterexamples of non-convergence. As [Theorem 3, Shamir 18\u2019] shows (although in scalar case), even when the assumptions are not satisfied, a convergence rate $O(exp(N) * log(1/\\epsilon))$ is possible. It will be an interesting future work to clearly delineate the boundary between convergence and non-convergence.\n\n* In Thm 2 and Claim 3, what happens if dimension $d_0$ is smaller? What is the reason that you had to restrict it to high dimension? Is it due to high variance with few samples?\n\n* In Thm 2, constants $d\u2019_0$ and $a$ hide the dependence of the result on p, but I would suggest stating the dependence of those parameters on p, and also dependence on other parameters such as N.\n\n* In Section 5, there is a statement \u201cThis negative result, a theoretical manifestation of the \u201cvanishing gradient problem\u201d, is circumvented by balanced initialization.\u201d Can you elaborate more on that? If my understanding is correct, there is still $\\sigma_min$ multiplier in Eq (9), which means that at near-zero regions, the gradient will still vanish.\n\n7: Good paper, accept", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 408 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper discusses the problem of gradient-based optimization methods in deep learning, which is the study of how to train neural networks. The main idea is that there are certain properties of critical points that imply convergence to global optimum, but these properties do not always hold in practice. The paper presents a trajectory-based approach to understanding this phenomenon, which focuses on the paths taken by the optimizer during training. The authors show that it is possible to converge to a global minimum in some cases, but it is not always possible. They also present a new approach to training deep linear neural networks that addresses some of the limitations of previous methods. The paper is technical in nature and assumes a good understanding of the mathematical concepts involved in deep learning. Overall, the paper provides a valuable contribution to the field of deep learning and offers new insights into the challenges of training neural networks.", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "In this paper, the preparation of a neural network for pruning and few-bit quantization is formulated as a variational inference problem. To this end, a quantizing prior that leads to a multi-modal, sparse posterior distribution over weights, is introduced and a differentiable Kullback-Leibler divergence approximation for this prior is derived. After training with Variational Network Quantization, weights can be replaced by deterministic quantization values with small to negligible loss of task accuracy (including pruning by setting weights to 0). The method does not require fine-tuning after quantization. Results are shown for ternary quantization on LeNet-5 (MNIST) and DenseNet (CIFAR-10).", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 99 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThis document discusses a method for reducing the redundancy in neural network models called Variational Network Quantization (VNQ). VNQ uses a Bayesian approach to simultaneously prune and quantize weights in neural networks. The method is trained using a multi-modal quantizing prior that penalizes weights with low variance unless they are close to one of the target values for quantization. The VNQ method is applicable to both pre-trained and training-from-scratch networks, and the target values for quantization can be either manually fixed or learned during training. The method is demonstrated on LeNet-5 and DenseNet models for image classification tasks.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Uncertainty in deep learning , Authors: Yarin Gal , 2016\n2.  Title: Bayesian compression for deep learning , Authors: Christos Louizos Karen Ullrich... , 2017\n3.  Title: Variational dropout sparsifies deep neural networks , Authors: Dmitry Molchanov Arsenii Ashukha... , 2017\n4.  Title: Structured bayesian pruning via log-normal multiplicative noise , Authors: Kirill Neklyudov Dmitry Molchanov... , 2017\n5.  Title: Soft weight-sharing for neural network compression , Authors: Karen Ullrich Edward Meeds... , 2017\n6.  Title: Trained ternary quantization , Authors: Chenzhuo Zhu Song Han... , 2016\n7.  Title: 2017) presented an approximation for the KL divergence under a (zero-centered) log uniform prior (Eq. (5)). Since our quantizing prior is essentially a composition of shifted log uniform priors, we construct a composition of the approximation given by Molchanov et al , Authors: Molchanov , 2017\n", "gt": "Our method is an extension of Sparse VD (Molchanov et al., 2017), originally used for network pruning. In contrast, we use a quantizing prior, leading to a multi-modal posterior suitable for fewbit quantization and pruning. Bayesian Compression and Structured Bayesian Pruning (Louizos et al., 2017; Neklyudov et al., 2017) extend Sparse VD to prune whole neurons or filters via groupsparsity constraints. Additionally, in Bayesian Compression the required bit-precision per layer is determined via the posterior variance. In contrast to our method, Bayesian Compression does not explicitly enforce clustering of weights during training and thus requires bit-widths in the range between 5 and 18 bits. Extending our method to include group-constraints for pruning is an interesting direction for future work. Another Bayesian method for simultaneous network quantization and pruning is soft weight-sharing (SWS) (Ullrich et al., 2017), which uses a Gaussian mixture model prior (and a KL term without trainable parameters such that the KL term reduces to the prior entropy). SWS acts like a probabilistic version of k-means clustering with the advantage of automatic collapse of unnecessary mixture components. Similar to learning the codebooks in our method, soft weight-sharing learns the prior from the data, a technique known as empirical Bayes. We cannot directly compare against soft weight-sharing since the authors do not report results on ternary networks. Gal et al. (2017) learn dropout rates by using a continuous relaxation of dropout\u2019s discrete masks (via the concrete distribution). The authors learn layer-wise dropout rates, which does not allow for dropout-rate-based pruning. We experimented with using the concrete distribution for learning codebooks for quantization with promising early results but so far we have observed lower pruning rates or lower accuracy compared to VNQ. A non-probabilistic state-of-the-art method for network ternarization is Trained Ternary Quantization (Zhu et al., 2016) which uses fullprecision shadow weights during training, but quantized forward passes.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 308 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThis paper presents a method called Variational Network Quantization (VNQ) for compressing neural networks. VNQ uses a Bayesian approach to simultaneously prune and quantize weights in neural networks. The method is trained using a variational objective that combines a multi-modal quantizing prior with a mean-field approximation of the posterior distribution over weights. The quantizing prior encourages weights to be drawn to specific quantization values, while the mean-field approximation allows for efficient learning of the posterior distribution. The method is applicable to both pre-trained and trained-from-scratch networks, and can be used for ternary or binary weight quantization. The authors demonstrate the effectiveness of VNQ on MNIST and CIFAR-10 datasets, showing that it achieves comparable or better accuracy than other compression methods while reducing the number of parameters in the network.", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "This paper presented a straightforward extension of the work done at [1, 2] for ternary networks through a multimodal quantising prior. It is generally well-written, with extensive preliminaries and clear equations. The visualizations also serve as a nice way to convey the behaviour of the proposed approach. The idea is interesting and well executed so I propose for acceptance. I only have a couple of minor questions: \n- For the KL-divergence approximation you report a maximum difference of 1 nat per weight that seems a bit high; did you experiment with the `naive` Monte Carlo approximation of the bound (e.g. as done at Bayes By Backprop) during optimization? If yes, was there a big difference in performance?\n- Was pre-training necessary to obtain the current results for MNIST? As far as I know, [1] and [2] did not need pre-training for the MNIST results (but did employ pre-training for CIFAR 10).\n- How necessary was each one of the constraints during optimization (and what did they prevent)? \n- Did you ever observe posterior means that do not settle at one of the prior modes but rather stay in between? Or did you ever had issues of the variance growing large enough, so that q(w) captures multiple modes of the prior (maybe the constraints prevent this)? How sensitive is the quantisation scheme?\n\nOther minor comments / typos:\n(1) 7th line of section 2.1 page 2, \u2018a unstructured data\u2019 -> \u2018unstructured data\u2019\n(2) 5th line on page 3, remove \u2018compare Eq. (1)\u2019 (or rephrase it appropriately).\n(3) Section 2.2, \u2019Kullback-Leibler divergence between the true and the approximate posterior\u2019; between implies symmetry (and the KL isn\u2019t symmetric) so I suggest to change it to e.g. \u2018from the true to the approximate posterior\u2019 to avoid confusion. Same for the first line of Section 3.3.\n(4) Footnote 2, the distribution of the noise depends on the random variable so I would suggest to change it to a general \\epsilon \\sim p(\\epsilon).\n(5) Equation 4 is confusing.\n\n7: Good paper, accept", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 335 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThis paper presents a method called Variational Network Quantization (VNQ) for compressing neural networks. The method uses a Bayesian approach and involves two main steps: pruning and quantization. Pruning involves removing irrelevant units from the network, which is done by determining the relevance of weights based on their magnitude. Quantization involves reducing the bit-precision of the weights, activations, or gradients, which is done by using a quantizing prior that penalizes weights of low variance unless they lie close to one of the target values for quantization. The method is trained using a variational objective that maximizes the evidence lower bound via stochastic gradient ascent and reparameterization tricks. The authors claim that their method is effective in reducing the size of the network while maintaining its accuracy.\n\nOne of the main contributions of this paper is the introduction of a new method for compressing neural networks that combines pruning and quantization. The method is based on a Bayesian approach and involves using a multi-modal quantizing prior that penalizes weights of low variance unless they lie close to one of the target values for quantization. The method is trained using a variational objective that maximizes the evidence lower bound via stochastic gradient ascent and reparameterization tricks. The authors claim that their method is effective in reducing the size of the network while maintaining its accuracy.\n\nThe paper also provides an extensive analysis of the method, including experiments on several datasets and comparisons with other state-of-the-art methods. The results show that the proposed method outperforms other methods in terms of compression ratio and accuracy. The authors also provide an analysis of the trade-off between compression ratio and accuracy and show that their method achieves a good balance between the two.\n\nOverall, this paper presents a novel and effective method for compressing neural networks. The method is based on a Bayesian approach and combines pruning and quantization to achieve a good balance between compression ratio and accuracy. The paper provides an extensive analysis of the method, including experiments on several datasets and comparisons with other state-of-the-art methods. The results show that the proposed method outperforms other methods in terms of compression ratio and accuracy.\n\nOne suggestion for future work would be to explore the use of the proposed method in other types of neural networks, such as recurrent neural networks or convolutional neural networks. Another suggestion would be to investigate", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "A core aspect of human intelligence is the ability to learn new tasks quickly and switch between them flexibly. Here, we describe a modular continual reinforcement learning paradigm inspired by these abilities. We first introduce a visual interaction environment that allows many types of tasks to be unified in a single framework. We then describe a reward map prediction scheme that learns new tasks robustly in the very large state and action spaces required by such an environment. We investigate how properties of module architecture influence efficiency of task learning, showing that a module motif incorporating specific design principles (e.g. early bottlenecks, low-order polynomial nonlinearities, and symmetry) significantly outperforms more standard neural network motifs, needing fewer training examples and fewer neurons to achieve high levels of performance. Finally, we present a meta-controller architecture for task switching based on a dynamic neural voting scheme, which allows new modules to use information learned from previouslyseen tasks to substantially improve their own learning efficiency.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 161 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper presents a study on the human brain's ability to adapt to new situations and learn new tasks. The authors investigate this problem using a neural module approach in which simple, task-specific decision modules are dynamically allocated on top of a largely-fixed underlying sensory system. The sensory system computes a general-purpose visual representation from which the decision modules read. The authors find that the fixed general purpose visual stream may be played by the ventral visual stream, modeled as a deep convolutional neural network. The algorithmic basis for how to efficiently learn and dynamically deploy visual decision modules remains far from obvious. The authors then introduce Reward Map Prediction (ReMaP) networks, an algorithm for continual reinforcement learning that is able to discover implicit task-specific interfaces in large action spaces like those of the TouchStream environment. The authors address two major algorithmic challenges associated with learning ReMaP modules. The first challenge is what module architectural motifs allow for efficient task interface learning, and the second challenge is how to efficiently deploy the learned modules in a real-world environment. The authors find that the architecture that performs best is one that incorporates certain intuitive design principles, such as early visual bottlenecks, low-order polynomial nonlinearities, and symmetry-inducing concatenations. The authors then evaluate the performance of the ReMaP algorithm on a variety of TouchStream tasks and find that it outperforms more standard neural network motifs in needing fewer training examples and fewer neurons to achieve high levels of performance.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Deep compositional question answering with neural module , Authors: Jacob Andreas Marcus Rohrbach... , 2015\n2.  Title: Net2net: Accelerating learning via knowledge transfer , Authors: Tianqi Chen Ian Goodfellow... , 2015\n3.  Title: Pathnet: Evolution channels gradient descent in super neural networks , Authors: Chrisantha Fernando Dylan Banarse... , 2017\n4.  Title: Learning to Reason: End-to-End Module Networks for Visual Question Answering , Authors: R. Hu J. Andreas... , 2017\n5.  Title: Overcoming catastrophic forgetting , Authors: James Kirkpatrick Razvan Pascanu... , 2016\n6.  Title: ImageNet classification with deep convolutional neural networks , Authors: A Krizhevsky I Sutskever... , 2012\n7.  Title: Learning to reinforcement learn , Authors: Jane X. Wang Zeb Kurth-Nelson... , 2016\n8.  Title: Improved multitask learning through synaptic intelligence , Authors: Friedemann Zenke Ben Poole... , 2017\n9.  Title: There are a total of 59 unique classes in this dataset , Authors: Yamins , 2014\n", "gt": "Modern deep convolutional neural networks have had significant impact on computer vision and artificial intelligence (Krizhevsky et al., 2012), as well as in the computational neuroscience of vision (Yamins & DiCarlo (2016)). There is a recent but growing literature on convnet-based neural modules, where they have been used for solving compositional visual reasoning tasks (Andreas et al., 2015; Hu et al., 2017). In this work we apply the idea of modules to solving visual learning challenges in a continual learning context. Existing works rely on choosing between a menu of pre-specified module primitives, using different module types to solve subproblems involving specific input-output datatypes, without addressing how these modules\u2019 forms are to be discovered in the first place. In this paper, we show a single generic module architecture is capable of automatically learning to solve a wide variety of different tasks in a unified action/state space, and a simple controller scheme is able to switch between such modules. Our results are also closely connected with the literature on lifelong (or continual) learning (Kirkpatrick et al., 2016; Rusu et al., 2016). A part of this literature is concerned with learning to solve new tasks without catastrophically forgetting how to solve old ones (Zenke et al., 2017; Kirkpatrick et al., 2016). The use of modules obviates this problem, but instead shifts the hard question to one of how newly-allocated modules can be learned effectively. The continual learning literature also directly addresses knowlege transfer to newly allocated structures (Chen et al., 2015; Rusu et al., 2016; Fernando et al., 2017), but largely addresses how transfer learning can lead to higher performance, rather than addressing how it can improve learning speed. ", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 277 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper presents a neural module approach to solve the problem of how humans support flexible learning and task switching in a real-world environment. The approach is based on a neural network called the TouchStream environment, which is designed to mimic the unconstrained nature of the real world. The network is composed of simple, task-specific decision modules that are learned on the fly in response to the environment's challenges. The network's performance is evaluated on a variety of tasks, including visually-driven tasks, and the results show that the network is able to learn and switch between tasks efficiently. The paper also presents a new algorithm called Reward Map Prediction (ReMaP) networks, which is able to discover implicit task-specific interfaces in large action spaces. The algorithm is able to learn the interfaces quickly and efficiently, and is able to improve the performance of the network on task switches. The paper also presents a meta-controller architecture based on a dynamic neural voting scheme, which is able to efficiently transfer knowledge between different modules on task switches. The paper's contributions to the field of artificial intelligence include a new approach to learning and task switching in a real-world environment, and a new algorithm for multitask learning. The paper also presents a new dataset, called the TouchStream environment, which is a large-scale, real-world dataset that is used to evaluate the performance of the proposed algorithms. The paper's findings have implications for the field of artificial intelligence, and could lead to the development of more efficient and effective algorithms for multitask learning and task switching in real-world environments.", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "Reading this paper feels like reading at least two closely-related papers compressed into one, with overflow into the appendix (e.g. one about the EMS module, one about the the recurrent voting, etc).\n\nThere were so many aspects/components, that I am not entirely confident I fully understood how they all work together, and in fact I am pretty confident there was at least some part of this that I definitely did not understand. Reading it 5-20 more times would most likely help.\n\n-The defs/eqns at the beginning of section 3.1 (Sc, CReLu, etc) were slightly hard to follow and I wonder whether there were any typos, e.g. was CReS meant to refer directly to Sc, but used the notation ${ReLu}^2$ instead? \n\nEach of these on its own would be easier to overlook, but there is a compounding effect here for me, as a reader, such that by further on in the paper, I am rather confused.\n\nI also wonder whether any of the elements described, have more \"standard\" interpretations/notations. For example, my slight confusion propagated further: after above point, I then did not have a clear intuition about $l_i$ in the EMS module. I get that symmetry has been built in, e.g. by the definitions of CReS and CReLu, etc, but I still don't see how it all works together, e.g. are late bottleneck architectures *exactly* the same as MLPs, but where inputs have simply been symmetrized, squared, etc? Nor do I have intuition about multiplicative symmetric interactions between visual features and actions, although I do get the sense that if I were to spend several hours implementing/writing out toy examples, it would clarify it significantly (in fact, I wouldn't be too surprised if it turns out to be fairly straightforward, as in my above comment indicating a seeming equivalence to simply multiplying two terms and taking the resulting sign). If the paper didn't need to be quite as dense, then I would suggest providing more elucidation for the reader, either with intuitions or examples or clearer relationships to more familiar formulations.\n\nLater, I did find that some of the info I *needed* in order to understand the results (e.g. exactly what is meant by a \"symmetry ablation\", how was that implemented?) was in fact in the appendices (of which there are over 8 pages).\n\nI do wonder how sensitive the performance of the overall system is to some of the details, like, e.g. the low-temp Boltzmann sampling rather than identity function, as described at the end of S2.\n\n8: Top 50% of accepted papers, clear accept", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 426 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper presents a neural module approach to solve the problem of flexible learning and task switching in a real-world environment. The approach is based on a neural network called the TouchStream environment, which is designed to support the flexible deployment of decision modules in response to changing tasks. The network is trained on a large dataset of visual images and is able to learn to recognize the general-purpose visual representation of the environment. The authors also introduce a new algorithm called Reward Map Prediction (ReMaP) networks, which is able to discover the implicit \"interfaces\" required to solve different tasks. The algorithm is able to switch between tasks efficiently and learn to deploy information from the base representation. The paper also describes the Dynamic Neural Voting (DynV) algorithm, which is able to dynamically allocate the network's resources to different tasks. The authors evaluate the performance of the network on a variety of tasks and show that it is able to learn and adapt to new tasks quickly and efficiently.\n\nIn terms of suggestions, it would be interesting to see how the approach could be extended to handle more complex tasks, such as those that require the network to reason about causality or make decisions based on uncertain or incomplete information. It would also be interesting to see how the approach could be applied to other domains, such as natural language processing or robotics.\n\nOne question that arises is how the network is able to generalize to new tasks that it has not seen before. While the authors show that the network is able to learn to recognize the general-purpose visual representation of the environment, it is not clear how well the network is able to generalize to new tasks that have different visual features or require different types of reasoning. It would be interesting to see how the network performs on a larger and more diverse set of tasks to evaluate its generalization ability.\n\nAnother question is how the network is able to handle tasks that require the network to reason about causality or make decisions based on uncertain or incomplete information. While the authors show that the network is able to learn to recognize the general-purpose visual representation of the environment, it is not clear how well the network is able to reason about causality or make decisions based on uncertain or incomplete information. It would be interesting to see how the network performs on tasks that require it to reason about causality or make decisions based on uncertain or incomplete information to evaluate its ability to handle such tasks.", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "The importance-weighted autoencoder (IWAE) approach of Burda et al. (2015) defines a sequence of increasingly tighter bounds on the marginal likelihood of latent variable models. Recently, Cremer et al. (2017) reinterpreted the IWAE bounds as ordinary variational evidence lower bounds (ELBO) applied to increasingly accurate variational distributions. In this work, we provide yet another perspective on the IWAE bounds. We interpret each IWAE bound as a biased estimator of the true marginal likelihood where for the bound defined on K samples we show the bias to be of order O(K\u22121). In our theoretical analysis of the IWAE objective we derive asymptotic bias and variance expressions. Based on this analysis we develop jackknife variational inference (JVI), a family of bias-reduced estimators reducing the bias to O(K\u2212(m+1)) for any given m < K while retaining computational efficiency. Finally, we demonstrate that JVI leads to improved evidence estimates in variational autoencoders. We also report first results on applying JVI to learning variational autoencoders.1", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 160 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nVariational autoencoders (VAEs) are a type of deep learning model used for generative modeling, representation learning, and probabilistic regression. VAEs consist of a probabilistic model and an approximate method for maximum likelihood estimation. The quality of the model's output is dependent on the quality of the model's input, and the lower bound on the true likelihood is only an approximation. The bound can be tight, but recent work has investigated richer variational families. The implementation is available at <https://github.com/Microsoft/jackknife-variational-inference>.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Perturbative black box variational inference , Authors: Robert Bamler Cheng Zhang... , 2017\n2.  Title: Nice: Non-linear independent components estimation , Authors: Laurent Dinh David Krueger... , 2014\n3.  Title: Adversarial message passing for graphical models , Authors: Theofanis Karaletsos , 2016\n4.  Title: Improving variational inference with inverse autoregressive flow , Authors: Diederik P Kingma Tim Salimans... , 2016\n5.  Title: Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks , Authors: Lars Mescheder Sebastian Nowozin... , 2017\n6.  Title: Masked autoregressive flow for density estimation , Authors: George Papamakarios Iain Murray... , 2017\n7.  Title: On the opportunities and pitfalls of nesting monte carlo estimators , Authors: Tom Rainforth Robert Cornish... , 2017\n8.  Title: Tighter variational bounds are not necessarily better , Authors: Tom Rainforth Tuan Anh Le... , 2017\n9.  Title: Markov chain monte carlo and variational inference: Bridging the gap , Authors: Tim Salimans Diederik Kingma... , 2015\n10.  Title: A collapsed variational bayesian inference algorithm for latent dirichlet allocation , Authors: Yee W Teh David Newman... , 2007\n11.  Title: Deep and hierarchical implicit models , Authors: Dustin Tran Rajesh Ranganath... , 2017\n", "gt": "The IWAE bound and other Monte Carlo objectives have been analyzed by independently by Rainforth et al. (2017a). Their analysis is more general than our IWAE analysis, but does not propose a method to reduce bias. Delta-method variational inference (DVI) proposed by Teh et al. (2007) is the closest method we are aware of and we discuss it in detail as well as provide novel results in Appendix B, page 14. Another exciting recent work is perturbative variational inference (Bamler et al., 2017) which considers different objective functions for variational inference; we are not sure whether there exists a deeper relationship to debiasing schemes. There also exists a large body of work that uses the ELBO objective but considers ways to enlarge the variational family. This is useful because the larger the variational family, the smaller the bias. Non-linear but invertible transformations of reference densities have been used initially for density estimation in NICE (Dinh et al., 2014) and for variational inference in Hamiltonian variational inference (Salimans et al., 2015). Around the same time the general framework of normalizing flows (Rezende & Mohamed, 2015) unified the previous works as some invertible continuous transformation of a distribution. Since then a large number of specialized flows with different computational requirements and flexibility have been constructed: inverse autoregressive flows (Kingma et al., 2016), masked autoregressive flows Papamakarios et al. (2017), and Householder flows (Tomczak & Welling, 2016). Another way to improve the flexibility of the variational family has been to use implicit models (Mohamed & Lakshminarayanan, 2016) for variational inference; this line of work includes adversarial variational Bayes (Mescheder et al., 2017), wild variational inference (Li & Liu, 2016), deep implicit models (Tran et al., 2017), implicit variational models (Husza\u0301r, 2017), and adversarial message passing approximations (Karaletsos, 2016).", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 295 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nVariational autoencoders (VAE) are a type of deep learning model that are used for generative modeling, representation learning, and probabilistic regression. They consist of a probabilistic model and an approximate method for maximum likelihood estimation. In the generative case, the model is defined as p(x) = \u222b p\u03b8(x|z) p(z) dz, where z is a latent variable and p(z) is a standard multivariate Normal distribution. To achieve an expressive marginal distribution p(x), we define p\u03b8(x|z) through a neural network. The maximum likelihood estimation of the parameters \u03b8 is intractable, so Kingma & Welling (2013) and Rezende et al. (2014) propose to instead maximize the evidence lower-bound (ELBO). The ELBO is a lower-bound to the true likelihood and is formed by taking the logarithm of the true likelihood and adding a regularization term. The VAE approach is empirically successful, but there are limitations to the quality of the model p\u03b8(x|z) and the tightness of the ELBO bound. Recent work has investigated richer variational families to address these limitations. The implementation of VAE is available at <https://github.com/Microsoft/jackknife-variational-inference>.", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "The authors introduce jackknife variational inference (JVI), a method for debiasing Monte Carlo objectives such as the importance weighted auto-encoder. Starting by studying the bias of the IWAE bound for approximating log-marginal likelihood, the authors propose to make use of debiasing techniques to improve the approximation. For the binarized MNIST the authors show improved approximations given the same number of samples from the auxiliary distribution q(z|x).\n\nJVI seems to be an interesting extension of, and perspective on, the IWAE bound (and other Monte Carlo objectives). Some questions and comments:\n\n* The Cremer et al. (2017) paper contains some errors when interpreting the IWAE bound as a standard ELBO with a more flexible variational approximation distribution. For example eq. (1) in their paper does not correspond to an actual distribution, it is not properly normalized. This makes the connection in their section 2.1 unclear. I would suggest citing the following paper instead for this connection and the relation to importance sampling (IS):\nNaesseth, Linderman, Ranganath, Blei, \"Variational Sequential Monte Carlo\", 2017.\n\n* Regarding the analysis of the IWAE bound the paper by Rainforth et al. (2017) mentioned in the comments seems very relevant. Also, because of the strong connection between IWAE and IS detailed in the Naesseth et al. (2017) paper it is possible to make use of a standard Taylor approximation/delta methods to derive Prop. 1 and Prop. 2, see e.g. Robert & Casella, \"Monte Carlo Statistical Methods\" or Liu's \"Monte Carlo Strategies for Scientific Computing\".\n\n* It could be worth mentioning that the JVI objective function is now no longer (I think?) a lower bound to the log-evidence.\n\n* Could the surprising issue (IWAE-learned, JV1-evaluated being better than JV1-learned, JV1-evaluated) in Table 1 be because of different local optima?\n\n* Also, we can easily get unbiased estimates of the evidence p(x) using IS and optimize this objective wrt to model parameters. The proposal parameters can be optimized to minimize variance, how do you think this compares to the proposed method?\n\nMinor comments:\n* p(x) -> p_\\theta(x)\n* In the last paragraph of section 1 it seems like you claim that the expressiveness of p_\\theta(x|z) is a limitation of VAE. It was a bit unclear to me what was actually a general limitation of maximum likelihood versus the approximation based on VAEs.\n* Last paragraph of section 1, \"strong bound\" -> \"tight bound\"\n* Last paragraph of section 2, citation missing for DVI\n\n7: Good paper, accept", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 407 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper presents a novel approach to generative modeling, representation learning, and probabilistic regression, called Variational Autoencoders (VAEs). VAEs are a class of expressive probabilistic deep learning models that are useful for generative modeling, representation learning, and probabilistic regression. The paper proposes a new method for maximum likelihood estimation of VAEs, called the Importance Weighted Autoencoder (IWAE) method, which is based on the Burda et al. (2015) method. The IWAE method is shown to be empirically successful but there are some limitations, such as the quality of the model p\u03b8(x) and the fact that the lower-bound is only an approximation to the true likelihood. The paper also presents an analysis of the IWAE bound and the bias and variance of the IWAE method. The paper concludes with a discussion of the limitations of the IWAE method and suggestions for future work.\n\nIn conclusion, the paper presents a novel approach to generative modeling, representation learning, and probabilistic regression, called Variational Autoencoders (VAEs). The paper proposes a new method for maximum likelihood estimation of VAEs, called the Importance Weighted Autoencoder (IWAE) method, which is based on the Burda et al. (2015) method. The IWAE method is shown to be empirically successful but there are some limitations, such as the quality of the model p\u03b8(x) and the fact that the lower-bound is only an approximation to the true likelihood. The paper also presents an analysis of the IWAE bound and the bias and variance of the IWAE method. The paper concludes with a discussion of the limitations of the IWAE method and suggestions for future work.", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "We consider the problem of generating configurations that satisfy physical constraints for optimal material nano-pattern design, where multiple (and often conflicting) properties need to be simultaneously satisfied. Consider, for example, the trade-off between thermal resistance, electrical conductivity, and mechanical stability needed to design a nano-porous template with optimal thermoelectric efficiency. To that end, we leverage the posterior regularization framework and show that this constraint satisfaction problem can be formulated as sampling from a Gibbs distribution. The main challenges come from the black-box nature of those physical constraints, since they are obtained via solving highly non-linear PDEs. To overcome those difficulties, we introduce Surrogate-based Constrained Langevin dynamics for black-box sampling. We explore two surrogate approaches. The first approach exploits zero-order approximation of gradients in the Langevin Sampling and we refer to it as Zero-Order Langevin. In practice, this approach can be prohibitive since we still need to often query the expensive PDE solvers. The second approach approximates the gradients in the Langevin dynamics with deep neural networks, allowing us an efficient sampling strategy using the surrogate model. We prove the convergence of those two approaches when the target distribution is log-concave and smooth. We show the effectiveness of both approaches in designing optimal nano-porous material configurations, where the goal is to produce nano-pattern templates with low thermal conductivity and reasonable mechanical stability.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 221 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper presents a method for generating samples under constraints in the black-box setting, where the potential function is unknown and can only be evaluated expensively. The method is based on constrained black-box sampling, which involves generating samples from a Gibbs distribution defined on a compact support. The paper introduces a new approach to constrained black-box sampling, called Surrogate Model Based Langevin dynamics, which uses a surrogate model to approximate the gradient of the potential function. The approach consists of two steps: learning the surrogate model using training data, and using the surrogate model in conjunction with a black-box potential to generate samples. The paper presents several objective functions and deep neural-network architectures for parameterizing the surrogate model. The approach is shown to be computationally efficient and effective in generating samples under constraints. The paper also presents applications of the method to the design of nanoporous configurations with improved thermoelectric efficiency.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Sampling from a log-concave distribution with compact support with proximal langevin monte carlo , Authors: Nicolas Brosse Alain Durmus... , 2017\n2.  Title: Finite-time analysis of projected langevin monte carlo , Authors: Sebastien Bubeck Ronen Eldan... , 2015\n3.  Title: Kernel exponential family estimation via doubly dual embedding , Authors: Bo Dai Hanjun Dai... , 2018\n4.  Title: Optimal rates for zero-order convex optimization: The power of two function evaluations , Authors: John C Duchi Michael I Jordan... , 2015\n5.  Title: Mirrored langevin dynamics , Authors: Ya-Ping Hsieh Ali Kavis... , 2018\n6.  Title: Estimation of non-normalized statistical models by score matching , Authors: Aapo Hyv\u00e4rinen , 2005\n7.  Title: Non-asymptotic results for langevin monte carlo: Coordinate-wise and black-box sampling , Authors: Lingqing Shen Krishnakumar Balasubramanian... , 2019\n8.  Title: Under review as a conference paper at ICLR , Authors: Qiang Wu Justin Guinney... , 2020\n9.  Title: The proof here is similar to the proof of Lemma 8 in (Bubeck et al., 2015). By the data processing inequality we have: KL(\u03bcT , Authors:  , 2015\n", "gt": "Zero-Order Methods. Zero-order optimization with Gaussian smoothing was studied in Nesterov & Spokoiny (2017) and Duchi et al. (2015) in the convex setting. Non-convex zero order optimization was also addressed in Ghadimi & Lan (2013). The closest to our work is the zero-order Langevin Shen et al. (2019) introduced recently for black-box sampling from log concave density. The main difference in our setting, is that the density has a compact support and hence the need to appeal to projected LMC (Bubeck et al., 2015) and Proximal LMC (Brosse et al., 2017). It is worth nothing that Hsieh et al. (2018) introduced recently mirror Langevin sampling that can also be leveraged in our framework. Gradients and Score functions Estimators. We used the approach of gradient distillation (Srinivas & Fleuret, 2018) and learning gradients of (Wu et al., 2010), since they are convenient for training on different constraints and they come with theoretical guarantees. However, other approaches can be also leveraged such as the score matching approach for learning the gradient of the log likelihood (Hyv\u00e4rinen, 2005) and other variants appealing to dual embeddings (Dai et al., 2018). Estimating gradients can be also performed using Stein\u2019s method as in (Li & Turner, 2017), or via maintaining a surrogate of the gradient as in Stein descent without gradient (Han & Liu, 2018). Optimization approaches. Due to space limitation, we restrict the discussion to the optimization methods that are most commonly and recently used for optimal material (or molecule) design.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 246 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe Related Work section for this paper should include the following papers:\n\n1. \"Sampling from a log-concave distribution with compact support with proximal langevin monte carlo,\" by Nicolas Brosse, Alain Durmus, and others (2017).\n2. \"Finite-time analysis of projected langevin monte carlo,\" by Sebastien Bubeck, Ronen Eldan, and others (2015).\n3. \"Kernel exponential family estimation via doubly dual embedding,\" by Bo Dai, Hanjun Dai, and others (2018).\n4. \"Optimal rates for zero-order convex optimization: The power of two function evaluations,\" by John C. Duchi, Michael I. Jordan, and others (2015).\n5. \"Mirrored langevin dynamics,\" by Ya-Ping Hsieh, Ali Kavis, and others (2018).\n6. \"Estimation of non-normalized statistical models by score matching,\" by Aapo Hyv\u00e4rinen (2005).\n7. \"Non-asymptotic results for langevin monte carlo: Coordinate-wise and black-box sampling,\" by Lingqing Shen, Krishnakumar Balasubramanian, and others (2019).\n8. \"Under review as a conference paper at ICLR,\" by Qiang Wu, Justin Guinney, and others (2020).\n9. \"The proof here is similar to the proof of Lemma 8 in (Bubeck et al., 2015). By the data processing inequality we have: KL(\u03bcT ,\" by an unknown author (2015).\n\nThe Related Work section should provide a brief overview of the papers and explain how they are related to the main paper. It should also highlight the key contributions of the main paper and how it differs from the related work.", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "The paper considers the problem of sampling points from a constrained set in R^d where the constraints can only be accessed in a zero order fashion. They consider the specific situation where the constraints are a solution of a complicated PDE solver and hence the derivatives or specific functional forms of the constraint cannot be obtained. They repose the problem as sampling from a Gibbs distribution whose potential contains constraints as penalties in a Lagrangian fashion. They now wish to sample from the Gibbs distribution using Langevin diffusion.  The Langevin process requires a derivative of the gradient. The setting does not allow for that and therefore the authors propose two approaches - \n\n1. Constructing the gradient from zeroth order entries of a gaussian smoothed potential (much like works of Nesterov et al on zero order optimization). \n2. Using a parameteric function class (like an RKHS or a neural network) to learn a function which well approximates the gradient of the constraints as well given zeroth order constraint evaluations. \n\n\nThe theoretical analysis seems quite rigorous as it begins by first providing a basic guarantee for constrained langevin sampling when gradients are computed with error. The non error gradient part of this analysis has been established before and the authors mention the references appropriately. I have a couple of questions regarding the precise statements of the theorem that i will ask towards the end of the review. Overall its hard to comment on the tightness of the analysis as the non-error versions are also unclear of the tightness of the bounds. Nevertheless the bounds achieved do not look much worse than the non-error counterparts and are easy to implement. The rest of the bounds focus on achieving low error in approximation of the gradients in various settings. Overall the theory in this part seems very loose in terms of bounds as exponential factors in dimensions start to appear and in that regard seems quite preliminary but its hard to comment on whether its natural or can be improved. \n\nSome questions I have regarding the theorem statements \n\nRegarding theorem 1 SPLMC convergence  (and corollaries of the theorem) -  I find it surprising that there is no lower bound assumption on eta in terms of K - only an upper bound. This seems wrong particularly as the theorem as stated then allows eta to be set extremely small while K is finite, in which case there should be no convergence theorems at all. The condition on eta should be a theta(f (K)) for some f type of condition like in the second part of the theorem. I would suggest the authors to look into the theorem - or claify why this is the case. \n\nI am confused by the presentation of the Shi et al results as there is no penalty appearing for the approximation error due to an RKHS, only a finite sample penalty. Does the result assume that phi belongs to the function class of the RKHS in question? Probably yes and in that case that should be specified. \n\n6: Weak Accept", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 510 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper presents a method for generating samples under constraints in the black-box setting, where the potential function is unknown and can only be evaluated at a high cost. The method is based on using a surrogate model to approximate the gradient of the potential function, and then using this approximation to perform constrained Langevin dynamics. The paper presents two approaches for learning the surrogate model: zero-order optimization and gradient-based optimization. The paper also presents two methods for performing constrained Langevin dynamics: constrained black-box sampling and constrained zero-order Langevin Monte Carlo. The paper provides theoretical guarantees for the convergence of these methods, and also presents experimental results demonstrating their effectiveness.\n\nOverall, the paper makes a significant contribution to the field of black-box optimization and constrained sampling. The use of surrogate models to approximate gradients is a promising approach for addressing the high cost of evaluating the potential function, and the use of constrained Langevin dynamics is a powerful method for sampling from the target distribution. The theoretical guarantees provided by the paper give confidence in the effectiveness of the proposed methods, and the experimental results provide further evidence of their usefulness.\n\nOne question that the paper raises is how to choose the appropriate parameters for the surrogate model and the constrained Langevin dynamics. The paper provides some guidance on this issue, but it would be interesting to see more detailed analysis of how different choices of these parameters affect the performance of the methods.\n\nAnother question that the paper raises is how to extend the methods to more complex settings, such as settings where the potential function is not convex or where there are multiple constraints. The paper provides some initial results in these settings, but it would be interesting to see more detailed analysis of how the methods can be adapted to these settings.\n\nOverall, the paper is well-written and well-organized, and it provides a clear and comprehensive overview of the proposed methods and their theoretical and experimental results. The paper is a valuable contribution to the field of black-box optimization and constrained sampling, and it raises important questions for future research.", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "This work addresses continual learning for non-stationary data, using Bayesian neural networks and memory-based online variational Bayes. We represent the posterior approximation of the network weights by a diagonal Gaussian distribution and a complementary memory of raw data. This raw data corresponds to likelihood terms that cannot be well approximated by the Gaussian. We introduce a novel method for sequentially updating both components of the posterior approximation. Furthermore, we propose Bayesian forgetting and a Gaussian diffusion process for adapting to non-stationary data. The experimental results show that our update method improves on existing approaches for streaming data. Additionally, the adaptation methods lead to better predictive performance for non-stationary data.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 109 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe document describes a method for a computer system to learn incrementally from a data stream, while adapting to changes in the data distribution and preventing catastrophic forgetting of previously learned tasks. The method is called continual learning and is typically described by a set of desiderata. The main focus of the document is on the development of an approximate Bayesian approach for training Bayesian neural networks with non-stationary data. The authors propose an online approximation consisting of a diagonal Gaussian distribution and a running memory, and they provide a novel sequential update method for both components. The approach is compared to variational continual learning and the Virtual Vector Machine, and it is shown that the proposed method is favorable. The authors also extend the online approximation by two alternative adaptation methods, and the results show that the proposed method is able to improve the stability-plasticity dilemma.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Streaming variational bayes , Authors: T. Broderick N. Boyd... , 2013\n2.  Title: Online variational Bayesian learning , Authors: Z. Ghahramani , 2000\n3.  Title: Overcoming catastrophic forgetting in neural networks , Authors: J. Kirkpatrick R. Pascanu... , 2017\n4.  Title: A practical Bayesian framework for backpropagation networks , Authors: D.J.C. MacKay , 1992\n5.  Title: Stochastic Models, Estimation and Control. Mathematics in science and engineering , Authors: P.S. Maybeck , 1982\n6.  Title: Virtual Vector Machine for Bayesian online classification , Authors: T.P. Minka R. Xiang... , 2009\n7.  Title: Variational continual learning , Authors: C.V. Nguyen Y. Li... , 2018\n8.  Title: A Bayesian approach to on-line learning , Authors: M. Opper , 1998\n9.  Title: Connectionist models of recognition memory: constraints imposed by learning and forgetting functions , Authors: R. Ratcliff , 1990\n10.  Title: Online structured laplace approximations for overcoming catastrophic forgetting , Authors: H. Ritter A. Botev... , 2018\n11.  Title: Catastrophic forgetting, rehearsal and pseudorehearsal , Authors: A. Robins , 1995\n12.  Title: Online model selection based on the variational bayes , Authors: M. Sato , 2001\n13.  Title: Continual learning with deep generative replay , Authors: H. Shin J.K. Lee... , 2017\n", "gt": "There are many Bayesian approaches to online learning, which differ mostly in the approximation of the posterior distribution at each time-step. Sequential Monte Carlo (Liu & Chen, 1998) approximates the posterior by a set of particles. Assumed Density Filtering (ADF) (Maybeck, 1982) and Bayesian online learning (Opper, 1998) are deterministic posterior approximations based on moment matching. Other deterministic approaches are based on Laplace\u2019s approximation (MacKay, 1992): Kirkpatrick et al. (2017) use multiple diagonal Gaussian posterior approximations of previous time-steps to regularise future tasks; Ritter et al. (2018) use a single (block-diagonal) posterior approximation, summarising all previous time-steps. The latter method is closer to Bayesian online inference, as it is an approximation of Eq. (1). Our work is based on online VB (Opper, 1998; Ghahramani, 2000; Sato, 2001; Broderick et al., 2013), which approximates the posterior at every time-step by minimising the KL-divergence between a parametric (here Gaussian) and the true posterior distribution. In contrast to online VB, we approximate the posterior by a Gaussian distribution and a running memory. Other approaches are based on various types of episodic memory, motivated by their empirical success in preventing catastrophic forgetting. The basic idea of rehearsal (Ratcliff, 1990) is to train on both the new data and a subset of previous data or pseudo samples (Robins, 1995; Shin et al., 2017; Kemker & Kanan, 2017) sampled from a generative model. The memory-based online inference methods most similar to our approach are VCL (Nguyen et al., 2018) and VVM (Minka et al., 2009). Both methods use a Gaussian distribution and a running memory to approximate the posterior. VCL uses heuristics such as random selection or the k-center method to update the memory.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 278 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nContinual learning (CL) is a field of study that deals with the development of algorithms that can learn from data streams that are non-stationary. This means that the data distribution changes over time, and the algorithm must be able to adapt to these changes. The main problem with non-stationary data is that it is difficult to balance the need to adapt to changes in the data distribution with the need to prevent forgetting previously learned information. This problem is also known as the stability-plasticity dilemma. The majority of current CL research is conducted in the context of online multi-task learning, where the main objective is to prevent catastrophic forgetting of previously learned tasks. This focus is reasonable since changes in the statistics of the data distribution are usually an artifact of learning different tasks sequentially. However, changes in the statistics of the data can also be real properties of the data-generating process. Examples include models of energy demand, climate analysis, financial market, or user-behavior analytics. In such applications, the statistics of the current data distribution are of particular interest. Old data may be outdated and can even deteriorate learning if the drift in the data distribution is neglected. Therefore, CL systems for non-stationary data require adaptation methods that deliberately forget outdated information. In this work, the authors develop an approximate Bayesian approach for training Bayesian neural networks (BNNs) incrementally with non-stationary streaming data. The authors propose an online approximation consisting of a diagonal Gaussian distribution and a running memory of previous data. The authors extend the online approximation by two alternative adaptation methods, thereby generalizing online variational Bayes with BNNs to non-stationary data. The authors compare their sequential update method to VCL in the online-inference setting on several popular datasets, demonstrating that their approach is favorable. Furthermore, the authors validate their adaptation methods on several datasets with concept drift, showing performance improvements compared to online variational Bayes without adaptation.", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "1. Summary:\nThis proposes considers neural networks training with non-stationary streaming data. To address online inference, the paper uses variational online updates and a running memory (coreset) summarising the data points seen so far, as recently used in the variational continual learning approach. First, the paper identifies a gap in this recent approach that coreset point selection process does not take into account the approximation quality. The paper develops a sound procedure to select the memory points, essentially to identify \u201cdifficult\u201d data points under the current posterior and put them into the running memory. Second, to handle \u201cconcept drift\u201d, the paper considers imposing some decaying on the likelihood of past data and derives post-hoc variational posterior updates for this case. Two contributions are validated on some streaming regression and classification tasks.\n\n2. Opinion and rationales\n\nI\u2019m leaning towards \u201caccept\u201d for this paper since it presents two interesting contributions (albeit of incremental novelty)  to the approximate inference area, it has clear execution and super clean presentation, and the experiments clearly demonstrate the values of the proposed approaches.\n\nI would like the paper to clarify the following:\n\na. I find the objective for selecting memory points interesting, but am wondering if using individual predictability terms by themselves are good enough to select these points. Perhaps, after some thoughts, memory is perhaps not the right word to characterise these points. \n\nIt seems to me (i) these terms indicate how well the current posterior predicts the data points and thus this objective will tend to favour points with low predictive likelihood to be selected. That is, these points are important when *all other points* are presented, (ii) there will be *no diversity* in the memory as illustrated in the 2d classification example.\n\nThis means the points selected here have quite different characteristics compared to coreset points or the full dataset in general. Coreset points ideally can compactly represent the full dataset and can be used for inference in place of the full dataset.\n\nThat said, the contribution presented here is very useful. Just that I\u2019m not sure how well this will work in more challenging continual learning set-up where diversity is important for a long sequence of diverse tasks.\n\nb. The paper presents a post-hoc modification to approximate posterior (\u2018s mean and variance) to account for the decay in the likelihood contribution from past data. I wonder if this post-hoc adjustment is identical to running approximate inference with the adjusted likelihood?\n\nc. the adaptation parameters need to be tuned/known in advance, which seems to be a disadvantage of the current approach. Is the update presented here somewhat robust to mis-specification of these parameters? How would these be tuned in a more practical setting in which data arrive sequentially and we might not know the underlying \u201cconcept\u201d.\n\n6: Weak Accept", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 465 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper presents a method for continual learning, which is the ability of machine learning models to learn incrementally from a data stream without catastrophic forgetting of previously learned tasks. The method is based on an approximate Bayesian approach for training Bayesian neural networks (BNNs) with non-stationary data. The authors propose an online approximation consisting of a diagonal Gaussian distribution and a running memory, and they provide a novel sequential update method for both components. The method is evaluated on several popular datasets with concept drift, showing performance improvements compared to online variational Bayes without adaptation. The paper also provides a thorough analysis of the stability-plasticity dilemma, which is the challenge of balancing the need to adapt to non-stationary data with the need to prevent forgetting of previously learned information. The authors suggest that their method can help address this dilemma by deliberately forgetting outdated information. Overall, the paper makes a significant contribution to the field of continual learning and provides a useful framework for future research in this area.\n\nMy german answer is:\n\nDer Beitrag pr\u00e4sentiert ein Verfahren zur kontinuierlichen Lernen, das die F\u00e4higkeit von maschinellen Lernmodellen beschreibt, incremental aus einem Datenstrom zu lernen ohne katastrophale Vergessen von zuvor gelernten Aufgaben. Das Verfahren basiert auf einem approximativen Bayesschen Ansatz zur Schulung von Bayesschen neuronalen Netzen (BNNs) mit nicht-station\u00e4ren Daten. Die Autoren schlagen einen onlineen Approximation vor, die aus einem Diagonal-Gau\u00df-Verteilung und einem laufenden Memory besteht und bieten eine neue sequentielle Aktualisierungsmethode f\u00fcr beide Komponenten. Das Verfahren wird auf mehreren bekannten Datens\u00e4tzen mit Konzeptdrift evaluiert, die eine Leistungssteigerung im Vergleich zu onlineen Variational Bayes ohne Adaptation zeigen. Der Beitrag bietet auch eine gr\u00fcndliche Analyse des Stabilit\u00e4ts-Plastizit\u00e4t-Dilemmas, das die Herausforderung darstellt, den Bedarf an Anpassung an nicht-station\u00e4re Daten mit dem Bedarf an Verhinderung des Vergessens von zuvor gelernten Informationen", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Recurrent Neural Networks (RNNs) are very successful at solving challenging problems with sequential data. However, this observed efficiency is not yet entirely explained by theory. It is known that a certain class of multiplicative RNNs enjoys the property of depth efficiency \u2014 a shallow network of exponentially large width is necessary to realize the same score function as computed by such an RNN. Such networks, however, are not very often applied to real life tasks. In this work, we attempt to reduce the gap between theory and practice by extending the theoretical analysis to RNNs which employ various nonlinearities, such as Rectified Linear Unit (ReLU), and show that they also benefit from properties of universality and depth efficiency. Our theoretical results are verified by a series of extensive computational experiments.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 130 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nIn this paper, the authors aim to extend previous research on Recurrent Neural Networks (RNNs) by analyzing their expressive power in terms of depth efficiency. They focus on RNNs with rectifier nonlinearities and use generalized tensor decompositions to show that these networks can efficiently represent a rich class of functions. The authors also compare RNNs with shallow networks and show that RNNs are exponentially more expressive. They also investigate the connection between tensor decompositions and multiplicative architectures and show that RNNs with rectifier nonlinearities are exponentially more efficient than shallow networks. The paper provides a theoretical understanding of the expressive power of RNNs and has implications for the design of deep learning architectures.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: On the expressive power of deep learning: A tensor analysis , Authors: Nadav Cohen Or Sharir... , 2016\n2.  Title: Boosting dilated convolutional networks with mixed tensor decompositions , Authors: Nadav Cohen Ronen Tamari... , 2018\n3.  Title: Hierarchical singular value decomposition of tensors , Authors: Lars Grasedyck , 2029\n4.  Title: Foundations of the PARAFAC procedure: Models and conditions for an \u201dexplanatory\u201d multimodal factor analysis , Authors: Richard A Harshman , 1970\n5.  Title: Expressive power of recurrent neural networks , Authors: Valentin Khrulkov Alexander Novikov... , 2018\n6.  Title: Speedingup convolutional neural networks using fine-tuned cp-decomposition , Authors: Vadim Lebedev Yaroslav Ganin... , 2015\n7.  Title: Tensorizing neural networks , Authors: Alexander Novikov Dmitrii Podoprikhin... , 2015\n8.  Title: Tensor-train decomposition , Authors: Ivan V Oseledets , 2011\n9.  Title: On multiplicative integration with recurrent neural networks , Authors: Yuhuai Wu Saizheng Zhang... , 2016\n10.  Title: Tensor-train recurrent neural networks for video classification , Authors: Yinchong Yang Denis Krompass... , 2017\n11.  Title: Long-term forecasting using tensor-train RNNs , Authors: Rose Yu Stephan Zheng... , 2017\n", "gt": "Tensor methods have a rich history of successful application in machine learning. (Vasilescu & Terzopoulos, 2002), in their framework of TensorFaces, proposed to treat facial image data as multidimensional arrays and analyze them with tensor decompositions, which led to significant boost in face recognition accuracy. (Bailey & Aeron, 2017) employed higher-order co-occurence data and tensor factorization techniques to improve on word embeddings models. Tensor methods also allow to produce more accurate and robust recommender systems by taking into account a multifaceted nature of real environments (Frolov & Oseledets, 2017). In recent years a great deal of work was done in applications of tensor calculus to both theoretical and practical aspects of deep learning algorithms. (Lebedev et al., 2015) represented filters in a convolutional network with CP decomposition (Harshman, 1970; Carroll & Chang, 1970) which allowed for much faster inference at the cost of a negligible drop in performance. (Novikov et al., 2015) proposed to use Tensor Train (TT) decomposition (Oseledets, 2011) to compress fully\u2013connected layers of large neural networks while preserving their expressive power. Later on, TT was exploited to reduce the number of parameters and improve the performance of recurrent networks in long\u2013term forecasting (Yu et al., 2017) and video classification (Yang et al., 2017) problems. In addition to the practical benefits, tensor decompositions were used to analyze theoretical aspects of deep neural nets. (Cohen et al., 2016) investigated a connection between various network architectures and tensor decompositions, which made possible to compare their expressive power. Specifically, it was shown that CP and Hierarchial Tucker (Grasedyck, 2010) decompositions correspond to shallow networks and convolutional networks respectively. Recently, this analysis was extended by (Khrulkov et al., 2018) who showed that TT decomposition can be represented as a recurrent network with multiplicative connections. This specific form of RNNs was also empirically proved to provide a substantial performance boost over standard RNN models (Wu et al., 2016). First results on the connection between tensor decompositions and neural networks were obtained for rather simple architectures, however, later on, they were extended in order to analyze more practical deep neural nets. It was shown that theoretical results can be generalized to a large class of CNNs with ReLU nonlinearities (Cohen & Shashua, 2016) and dilated convolutions (Cohen et al., 2018), providing valuable insights on how they can be improved.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 385 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nRecurrent Neural Networks (RNNs) have been established as a powerful deep learning technique for processing sequential data, such as text, audio, or video. However, the expressive power of RNNs and their ability to efficiently represent complex functions with a relatively small number of parameters is not yet fully understood. In recent work, Cohen et al. (2016) showed that Convolutional Neural Networks (CNNs) with product pooling are exponentially more expressive than shallow networks. Similarly, Khrulkov et al. (2018) showed that Recurrent Neural Networks with multiplicative recurrent cells are also exponentially more expressive than shallow networks. However, the expressive power of RNNs with rectifier nonlinearities, which are often used in practice, is not yet well understood. In this paper, the authors aim to extend previous analysis to RNNs with rectifier nonlinearities and show that they are also exponentially more expressive than shallow networks. The authors use the machinery of generalized tensor decompositions to show that RNNs with rectifier nonlinearities are universally capable of representing any function that can be represented by a shallow network, but with a smaller number of parameters. The authors also show that RNNs with rectifier nonlinearities are more efficient than RNNs with multiplicative recurrent cells in terms of the number of parameters required to achieve a given level of expressive power. The results of this paper provide new insights into the expressive power of RNNs and have implications for the design of deep learning architectures for processing sequential data.", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "The authors extend the theoretical results of a paper previously presented in the last edition of ICLR (2018), where it was demonstrated that Recurrent Neural Network can be interpreted as a tensor network decomposition based on the Tensor-Train (TT, Oseledets et al, 2011).\n\nThe paper is well written and have a good structure. However, I found that some deep concepts are not well introduced, and maybe other more trivial results are discussed with unnecessary details. The following comments could help authors to improve the quality of presentation of their paper:\n-\tSection 3.1 (Score Functions and Feature Tensor) is a bit short and difficult to read. \no\tMaybe, a more motivating introduction could be included in order to justify the definition of score functions (eq. 2). \no\tIt would be also nice to state that, according to eq. (3), the feature tensor is a rank-1 tensor. \no\tI would suggest moving the definition of outer product to the Appendix, since most readers know it very well.\no\tIt is said that eq. 2 possesses the universal approximation property (it can approximate any function with any prescribed precision given sufficiently large M). It is not clear which is the approximation function.\n-       When scores functions are presented (eq. 2) it is written the term \"logits\" between brackets. Could you please clarify why this term is introduced here? Usually, logit of a probability p is defined as L(p)=p/(1-p). What is the usage of this term in this work? \n-      I think the theory is presented for a model with the two-classes only but used for multiple classes in the experimental sections. It should be necessary to make some comment about this in the paper.\n-      Details about how the RNN based on TT is applied must be added. More specifically, the authors should provide answers to clarify the following questions: \n(i) Are patches overlapped or non-overlapped? \n(ii) What value of M is used? and is there any general rule for this choice? \n(iii) How the classification in the 10-classes is obtained? Are you using a softmax function in the last layer? Are you using one weight tensor W_c per class (c=1,2,...,10). Please provide these technical details. \n(iv) Please, specify which nonlinear activation sigma is used in the feature map f_\\theta(x).\n(v) How many feature maps are used? and, Are the matrix A and vector b learned from training dataset or only the TT-cores need to be learned? \n7: Good paper, accept", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 409 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper presents a study on the use of Recurrent Neural Networks (RNNs) for processing sequential data. The authors aim to extend previous research on the expressive power of RNNs with multiplicative recurrent cells by examining the use of rectifier nonlinearities. They apply the machinery of generalized tensor decompositions to show that RNNs with rectifier nonlinearities are also universally expressive. The paper also introduces architectures inspired by tensor decompositions and shows that these architectures can be used to process sequential data. The authors provide a detailed analysis of the expressive power of RNNs and compare them to shallow networks. They also present experimental results that support their theoretical findings. Overall, the paper provides a valuable contribution to the field of deep learning and RNNs.\n\nOne suggestion for future work would be to extend the analysis to other types of nonlinearities, such as softplus or ELU, to see if they also exhibit universal expressiveness. Additionally, it would be interesting to see how these results apply to more complex RNN architectures, such as Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU) networks. Another question that arises is how these results can be applied to other areas of machine learning, such as natural language processing or speech recognition.\n\nIn conclusion, the paper presents a thorough analysis of the expressive power of RNNs with rectifier nonlinearities. The authors provide a detailed theoretical framework and experimental results that support their findings. The paper makes a valuable contribution to the field of deep learning and RNNs.", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Simultaneous machine translation models start generating a target sequence before they have encoded the source sequence. Recent approaches for this task either apply a fixed policy on a state-of-the art Transformer model, or a learnable monotonic attention on a weaker recurrent neural network-based structure. In this paper, we propose a new attention mechanism, Monotonic Multihead Attention (MMA), which extends the monotonic attention mechanism to multihead attention. We also introduce two novel and interpretable approaches for latency control that are specifically designed for multiple attention heads. We apply MMA to the simultaneous machine translation task and demonstrate better latency-quality tradeoffs compared to MILk, the previous state-of-the-art approach. We analyze how the latency controls affect the attention span and we study the relationship between the speed of a head and the layer it belongs to. Finally, we motivate the introduction of our model by analyzing the effect of the number of decoder layers and heads on quality and latency.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 157 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThis paper proposes a novel attention mechanism called monotonic multihead attention (MMA) that combines the high translation quality of multihead attention and the low latency of monotonic attention. MMA is designed for the Transformer model and has two variants: Hard MMA (MMA-H) and Infinite Lookback MMA (MMA-IL). MMA-H is designed for streaming systems where the attention span must be limited, while MMA-IL emphasizes translation quality. The paper also introduces two novel latency regularization methods and analyzes the relationship between the speed of a head and the layer it belongs to. The main contributions of this paper are: (1) a new attention mechanism that enables the Transformer model to perform online decoding, (2) better latency/quality trade-offs compared to the previous state-of-the-art model on two standard translation benchmarks, and (3) analyses on how the model controls the attention span and the relationship between the speed of a head and the layer it belongs to.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Incremental decoding and training methods for simultaneous translation in neural machine translation , Authors: Fahim Dalvi Nadir Durrani... , 2018\n2.  Title: Don\u2019t until the final verb wait: Reinforcement learning for simultaneous machine translation , Authors: Alvin Grissom II He He... , 2014\n3.  Title: Learning to translate in real-time with neural machine translation , Authors: Jiatao Gu Graham Neubig... , 2017\n4.  Title: Learning hard alignments with variational inference , Authors: Dieterich Lawson Chung-Cheng Chiu... , 2018\n5.  Title: Learning online alignments with continuous rewards policy gradient , Authors: Yuping Luo Chung-Cheng Chiu... , 2017\n6.  Title: STACL: Simultaneous translation with implicit anticipation and controllable latency using prefix-to-prefix framework , Authors: Mingbo Ma Liang Huang... , 2019\n", "gt": "Recent work on simultaneous machine translation falls into three categories. In the first one, models use a rule-based policy for reading input and writing output. Cho & Esipova (2016) propose a WaitIf-* policy to enable an offline model to decode simultaneously. Ma et al. (2019) propose a wait-k policy where the model first reads k tokens, then alternates between read and write actions. Dalvi et al. (2018) propose an incremental decoding method, also based on a rule-based schedule. In the second category, a flexible policy is learnt from data. Grissom II et al. (2014) introduce a Markov chain to phrase-based machine translation models for simultaneous machine translation, in which they apply reinforcement learning to learn the read-write policy based on states. Gu et al. (2017) introduce an agent which learns to make decisions on when to translate from the interaction with a pre-trained offline neural machine translation model. Luo et al. (2017) used continuous rewards policy gradient for online alignments for speech recognition. Lawson et al.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 166 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper proposes a new attention mechanism called Monotonic Multihead Attention (MMA) for Transformer models, which combines the high translation quality from multihead attention and low latency from monotonic attention. The authors introduce two variants of MMA, Hard MMA (MMA-H) and Infinite Lookback MMA (MMA-IL), and two novel latency regularization methods. The first encourages the model to be faster by directly minimizing the average latency, while the second encourages the attention heads to maintain similar positions, preventing the latency from being dominated by a single or a few heads. The MMA model outperforms the previous state-of-the-art model, Monotonic Infinite Lookback Attention (MILk), on two standard translation benchmarks, IWSLT15 English-Vietnamese (En-Vi) and WMT15 German-English (DeEn), in terms of better latency/quality tradeoffs. The paper also provides analyses on how the MMA model is able to control the attention span and on the relationship between the speed of a head and the layer it belongs to.", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "This paper proposes a fully transformer-based monotonic attention framework that extends the idea of MILK. Though the idea of monotonic multi-head attention sounds interesting, I still have some questions below:\n\nAbout the method:\n   1. Is that possible that the MMA would have worse latency than MILK since all the attention heads need to agree to write while MILK only has one attention head?\n   2. Is there any attention order between different attention head?\n   3. I think the MMA only could control the latency during training time, which would produce different models with different latency. Is there any way that enables MMA to control the latency during inference time? Can we change the latency for on given model by tuning the requirements mentioned in (1)?\n\nAbout the experiments:\n    1. Do you have any explanation of why both MMA-H and MMA-IL have better BLEU when AL is small? The results in fig 2 seem counterintuitive. \n    2. I suggest the authors do more analysis of the difference between different attention heads to prove the effectiveness of MMA. \n    3. For the left two figures in fig 4, which one is the baseline, and which one is the proposed model?\n\nI also suggest the authors present more real sample analysis and discussions about the experiments.\n6: Weak Accept", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 213 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper proposes a new approach to simultaneous machine translation, which combines the benefits of monotonic attention and multihead attention in the Transformer model. The authors introduce two variants of the model, Hard MMA (MMA-H) and Infinite Lookback MMA (MMA-IL), which are designed to balance translation quality and latency. The paper also presents two novel latency regularization methods and analyzes the impact of the number of decoder layers and decoder heads on the model's performance. The main contributions of the paper are: (1) a novel monotonic attention mechanism that enables the Transformer model to perform online decoding, (2) better latency/quality trade-offs compared to the previous state-of-the-art model on two standard translation benchmarks, and (3) analyses on how the model controls the attention span and the relationship between the speed of a head and the layer it belongs to. Overall, the paper presents a promising approach to simultaneous machine translation and provides valuable insights into the design of attention-based models.\n\nMy japanese answer is:\n\n\u3053\u306e\u8ad6\u6587\u3067\u306f\u3001\u30e2\u30ce\u30c8\u30cb\u30c3\u30af\u6ce8\u610f\u3068\u30de\u30eb\u30c1\u30d8\u30c3\u30c9\u6ce8\u610f\u3092\u7d44\u307f\u5408\u308f\u305b\u305f\u65b0\u3057\u3044\u624b\u6cd5\u3092\u63d0\u6848\u3057\u3066\u3044\u307e\u3059\u3002\u3053\u306e\u624b\u6cd5\u306f\u3001\u30c8\u30e9\u30f3\u30b9\u30d5\u30a9\u30fc\u30de\u30fc\u30e2\u30c7\u30eb\u306e\u4e2d\u3067\u3001\u7ffb\u8a33\u306e\u54c1\u8cea\u3068\u9045\u5ef6\u306e\u30d0\u30e9\u30f3\u30b9\u3092\u53d6\u308b\u3053\u3068\u3092\u76ee\u7684\u3068\u3057\u3066\u3044\u307e\u3059\u3002\u8ad6\u6587\u3067\u306f\u3001\u30cf\u30fc\u30c9MMA\uff08MMA-H\uff09\u3068\u30a4\u30f3\u30d5\u30a3\u30cb\u30c3\u30c8\u30ed\u30fc\u30d0\u30c3\u30afMMA\uff08MMA-IL\uff09\u3068\u3044\u30462\u3064\u306e\u30e2\u30c7\u30eb\u306e\u30d0\u30ea\u30a8\u30fc\u30b7\u30e7\u30f3\u3092\u5c0e\u5165\u3057\u3001\u3053\u308c\u3089\u306e\u30e2\u30c7\u30eb\u304c\u7ffb\u8a33\u306e\u54c1\u8cea\u3068\u9045\u5ef6\u306e\u30c8\u30ec\u30fc\u30c9\u30aa\u30d5\u3092\u3069\u306e\u3088\u3046\u306b\u30d0\u30e9\u30f3\u30b9\u3059\u308b\u304b\u3092\u5206\u6790\u3057\u3066\u3044\u307e\u3059\u3002\u8ad6\u6587\u3067\u306f\u3082\u30462\u3064\u306e\u65b0\u3057\u3044\u9045\u5ef6\u8abf\u6574\u624b\u6cd5\u3082\u63d0\u6848\u3057\u3066\u304a\u308a\u3001\u30c7\u30b3\u30fc\u30c0\u30fc\u30ec\u30a4", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Robustness is an important property to guarantee the security of machine learning models. It has recently been demonstrated that strong robustness certificates can be obtained on ensemble classifiers generated by input randomization. However, tight robustness certificates are only known for symmetric norms including `0 and `2, while for asymmetric norms like `1, the existing techniques do not apply. By converting the likelihood ratio into a one dimensional mixed random variable, we derive the first tight `1 robustness certificate under isotropic Laplace distributions in binary case. Empirically, the deep networks smoothed by Laplace distributions yield the state-of-the-art certified robustness in `1 norm on CIFAR-10 and ImageNet.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 105 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper presents a new method for generating ensembles of fixed black-box classifiers based on input randomization. This method allows for the derivation of tight robustness certificates with only the ensemble prediction values and randomization parameters. The certificates are derived for the `2 or `0 norms and are shown to be tight in the binary classification setting. In the multi-class classification setting, the proposed robustness certificate outperforms the previous certificate proposed by Lecuyer et al. (2019). The proposed method is based on the novel modeling scheme that generates ensembles of a fixed black-box classifier based on input randomization. The method is applied to the problem of certifying the robustness of deep learning models and is shown to be effective in this setting.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Sorting out lipschitz function approximation , Authors: Cem Anil James Lucas... , 2018\n2.  Title: A unified view of piecewise linear neural network verification , Authors: Rudy Bunel Ilker Turkaslan... , 2018\n3.  Title: Maximum resilience of artificial neural networks , Authors: Chih-Hong Cheng Georg N\u00fchrenberg... , 2017\n4.  Title: Parseval networks: Improving robustness to adversarial examples , Authors: Moustapha Cisse Piotr Bojanowski... , 2017\n5.  Title: Output range analysis for deep neural networks , Authors: Souradeep Dutta Susmit Jha... , 2017\n6.  Title: Regularisation of neural networks by enforcing lipschitz continuity , Authors: Henry Gouk Eibe Frank... , 2018\n7.  Title: On the effectiveness of interval bound propagation for training verifiably robust models , Authors: Sven Gowal Krishnamurthy Dvijotham... , 2018\n8.  Title: Differentiable abstract interpretation for provably robust neural networks , Authors: Matthew Mirman Timon Gehr... , 2018\n9.  Title: Fast and effective robustness certification , Authors: Gagandeep Singh Timon Gehr... , 2018\n10.  Title: Evaluating robustness of neural networks with mixed integer programming , Authors: Vincent Tjeng Kai Y Xiao... , 2017\n11.  Title: Lipschitz-margin training: Scalable certification of perturbation invariance for deep neural networks , Authors: Yusuke Tsuzuku Issei Sato... , 2018\n12.  Title: Enresnet: Resnet ensemble via the feynman-kac , Authors: Bao Wang Binjie Yuan... , 2018\n13.  Title: Towards fast computation of certified robustness for relu networks , Authors: Tsuiwei Weng Huan Zhang... , 2018\n14.  Title: Efficient neural network robustness certification with general activation functions , Authors: Huan Zhang Tsui-Wei Weng... , 2018\n", "gt": "Robustness of a model can be defined in various aspects. For example, Feynman-Kac Formalism can be used to improve robustness (Wang et al., 2018). In this paper, we focus on the classification setting, where the goal is to provide guarantee of a constant prediction among a small region specified via some metric. The robustness certificate can be either exact or conservative, so long as a constant prediction is guaranteed in the certified region. Note that the certification of a completely black-box model requires checking the prediction values at every point around the point of interest, which is clearly infeasible. A practical certification algorithm inevitably has to specify and leverage the functional structure of the classifier in use to reduce the required computation. Exact certificates. The exact certificate of deep networks is typically derived for the networks with a piecewise linear activation function such as ReLU. Such networks have an equivalent mixed integer linear representation (Cheng et al., 2017; Lomuscio & Maganti, 2017; Dutta et al., 2017; Bunel et al., 2018). Hence, one may apply mixed integer linear programming to find the worst case adversary within any convex polyhedron such as an `1-ball or `\u221e-ball. Despite the elegant solution, the complexity is, in general, NP-hard and the algorithms are not scalable to large problems(Tjeng et al., 2017). Conservative certificates. A conservative certificate can be more scalable than the exact methods, since one can trade-off the accuracy of certification with efficiency (Gouk et al., 2018; Tsuzuku et al., 2018; Cisse et al., 2017; Anil et al., 2018; Hein & Andriushchenko, 2017). For example, one can relax the search of the worst case adversary as a simpler optimization problem that only bounds the effect of such adversary. Alternatively, people also consider the robustness problem in a modular way, where the robustness guarantee can be derived iteratively for each layer in the deep networks by considering the feasible values for each hidden layer (Gowal et al., 2018; Weng et al., 2018; Zhang et al., 2018; Mirman et al., 2018; Singh et al., 2018).", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 339 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper presents a new method for generating ensembles of fixed black-box classifiers based on input randomization. The method is based on the novel modeling scheme that generates ensembles of a fixed black-box classifier based on input randomization. The method is applied to the problem of certifying the robustness of deep learning models, which is a pre-requisite when AI-generated decisions may have important consequences. The existing approaches to certifying the robustness of deep learning models rely on empirical demonstration against specific adversarial attack algorithms, but these methods can give a false sense of security. The proposed method is based on the derivation of tight robustness certificates with only the ensemble prediction values and randomization parameters. The method is applied to the binary classification setting and is shown to be effective in practice. The proposed method is also compared to existing methods and is shown to be more effective in practice.", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "In this paper, the author derived a tight ell_1, which is not the symmetric norm, robustness certificates under isotropic Laplace distributions. Experimentally, the authors showed that the deep networks smoothed \nby Laplace distributions yield the state-of-the-art certified robustness in ell_1 norm on the CIFAR-10 \nand ImageNet. To find the ell_1 certificate, the authors first identified the tight robustness certificate, for attacking the model in one particular direction, say the first direction. To show that any other perturbation directions cannot lead to a worse result, the authors convert the d dimensional likelihood function into a one-dimensional function, and the authors used relaxation for different perturbations and show that the worst-case result is bounded by the previously identified direction.  However, I have the following concerns about this work:\n\n1. Theoretically, the authors only showed the certificate is tight for binary classification. I would suggest\nthe author change their claim in the abstract.\n\n2. What is M on page 3 which is used without definition after definition 1?\n\n3. Can you give a concrete continuous probability distribution that leads to the scenario in Fig.~3?\n\n4. Can you extend the analysis to a multi-class classification scenario?\n\n5. Besides randomized smoothing on the input images, recently Wang et al showed that randomize the deep nets can\nalso improve the deep nets and they gave it a nice theoretical interpretation. Here is the reference: Bao Wang, Binjie Yuan, Zuoqiang Shi, Stanley J. Osher. ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies, arXiv:1811.10745, NeurIPS, 2019\n\nOverall, since this work is a straightforward integration of some existing work, I think this\npaper lack novelty. Please address the above questions in rebuttal.\n3: Weak Reject", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 280 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper presents a new method for generating ensembles of fixed black-box classifiers based on input randomization. This method allows for the derivation of tight robustness guarantees for the classifiers under isotropic Laplace distributions. The authors show that their method is more effective than previous approaches based on Gaussian distributions. The paper also presents a new result on the characterization of the overlapped region between the `1 norm balls centered at the origin, which is a key step for proving tight certificates for the `1 norm. The authors demonstrate the effectiveness of their method on several benchmark datasets and show that it outperforms previous methods in terms of both accuracy and robustness.\n\nIn terms of suggestions, it would be interesting to see how the method performs on more complex models and datasets. Additionally, it would be useful to have a more detailed analysis of the trade-off between accuracy and robustness achieved by the method.\n\nOverall, the paper presents a novel and effective approach for generating ensembles of fixed black-box classifiers with tight robustness guarantees. The method is shown to outperform previous approaches on several benchmark datasets and could be useful in applications where the robustness of the classifiers is critical.", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Deep Reinforcement Learning (Deep RL) has been receiving increasingly more attention thanks to its encouraging performance on a variety of control tasks. Yet, conventional regularization techniques in training neural networks (e.g., L2 regularization, dropout) have been largely ignored in RL methods, possibly because agents are typically trained and evaluated in the same environment. In this work, we present the first comprehensive study of regularization techniques with multiple policy optimization algorithms on continuous control tasks. Interestingly, we find conventional regularization techniques on the policy networks can often bring large improvements on the task performance, and the improvement is typically more significant when the task is more difficult. We also compare with the widely used entropy regularization and find L2 regularization is generally better. Our findings are further confirmed to be robust against the choice of training hyperparameters. We also study the effects of regularizing different components and find that only regularizing the policy network is typically the best option. We hope our study provides guidance for future practices in regularizing policy optimization algorithms.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 172 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe article discusses the importance of regularization techniques in the field of deep learning, particularly in the context of training neural networks. Regularization is a method used to prevent overfitting in neural networks, which occurs when a model becomes too complex and performs well on the training data but poorly on new, unseen data. The two most commonly used regularization techniques in deep learning are L2 regularization and dropout. However, these techniques are not commonly used in the field of deep reinforcement learning (RL), which is the focus of the article. The authors of the article conducted a study to determine the effectiveness of various regularization techniques in RL, and found that some of these techniques, such as L2 regularization, can significantly improve the performance of RL algorithms on difficult tasks. The article concludes that regularization techniques can be as important as high-level RL algorithms in terms of boosting performance, and provides a comprehensive study of common regularization methods in policy optimization algorithms.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Control regularization for reduced variance reinforcement learning , Authors: Richard Cheng Abhinav Verma... , 1905\n2.  Title: Addressing function approximation error in actorcritic methods , Authors: Scott Fujimoto Herke van Hoof... , 2018\n3.  Title: Imformation asymmetry in kl-regularized rl , Authors: Alexandre Galashov Siddhant Jayakumar... , 2019\n4.  Title: Continuous control with deep reinforcement learning , Authors: Timothy Lillicrap Jonathan Hunt... , 2016\n5.  Title: Continuous control with deep reinforcement learning , Authors: Timothy P Lillicrap Jonathan J Hunt... , 2015\n6.  Title: Td-regularized actor-critic methods , Authors: Simone Parisi Voot Tangkaratt... , 2019\n7.  Title: Trust region policy optimization , Authors: John Schulman Sergey Levine... , 2015\n8.  Title: High-dimensional continuous control using generalized advantage estimation , Authors: John Schulman Philipp Moritz... , 2016\n9.  Title: Proximal policy optimization algorithms , Authors: John Schulman Filip Wolski... , 2017\n", "gt": "Regularization in Deep RL. Conventional regularization methods have rarely been applied in deep RL. One rare case of such use is in Deep Deterministic Policy Gradient (DDPG) (Lillicrap et al., 2016), where Batch Normalization is applied to all layers of the actor \u00b5 network and layers of the critic Q network prior to the action input, and L2 regularization is applied to the critic Q network because it tends to have overestimation bias (Fujimoto et al., 2018). Some recent studies have developed more complicated regularization approaches to continuous control tasks. Cheng et al. (2019) regularizes the stochastic action distribution \u03c0(a|s) using a suboptimal control prior. The regularization weight at a given state is adjusted based on the temporal difference (TD) error. The larger the TD error, the more the action distribution moves towards the prior. Galashov et al. (2019) introduces a default policy that receives limited information as a regularizer. The information asymmetry between the behavior policy and the default policy helps to accelerate convergence and improve performance. Parisi et al. (2019) introduces TD error regularization to penalize inaccurate value estimation and Generalized Advantage Estimation (GAE) (Schulman et al., 2016) regularization to penalize GAE variance. However, most of these regularizations are rather complicated (Galashov et al., 2019), specifically designed for certain algorithms (Parisi et al., 2019), or need prior information (Cheng et al., 2019). Also, these techniques consider regularizing the output\nof the network, while conventional regularization methods mostly directly regularize the parameters.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 243 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nRegularization is a key technique in successfully training a neural network, particularly when preventing overfitting. In deep reinforcement learning (RL), conventional regularization methods such as L2 regularization and dropout are largely absent or underutilized in past research. However, a recent study has found that conventional regularization methods can be very effective in improving the performance of RL agents on continuous control tasks. The study investigated four popular policy optimization algorithms, namely SAC, PPO, TRPO, and the synchronous version of Advantage Actor Critic (A2C), on multiple continuous control tasks. A variety of conventional regularization techniques were considered, including L2/L1 weight regularization, dropout, weight clipping, and Batch Normalization. The results showed that many of the conventional regularization techniques, when imposed to the policy networks, can still bring up the performance, sometimes significantly. Among those regularizers, L2 regularization, perhaps the most simple one, tends to be the most effective for all algorithms and generally outperforms entropy regularization. The study also found that imposing proper regularization can sometimes save the effort of tuning other training hyperparameters. The findings suggest that neural network training techniques such as regularization can be as important as high-level RL algorithms in terms of boosting performance.", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "The authors present that regularization methods mostly help to improve the agents' performance in terms of final scores. Specifically, they show that direct regularizations on model parameters, such as the standard case of L2 or L1 regularization, generally improve the agent performance. They also show that these regularizations, in their study, is more proper than entropy regularization. The authors also show that, in the presence of such regularizations, the learning algorithms become less sensitive to the hyperparameters. \n\nFew comments:\n1) The paper is well written and easy to follow. I appreciate it. I found the writing of the paper has a bit of repetition. The authors might find it slightly more proper to remove some of the repetitions (e.g. section 4.2)\n2) I would suggest rephrasing the last two sentences of the second paragraph in related work: \"Also, these techniques consider ...\". Regularizing the output also regularizes the parameters, I think the authors' point was \"directly regularize\" the parameters. \n3) In the \"Entropy Regularization\" part of section 3, I guess the Hs has not been defined. \n4) Repeated \"the\" in the last paragraph of section 4.1 (despite it already incorporates the the maximization of)\n5) The authors used the term \"not converge\" multiple times. While it is hard from the plots to see whether the series converges or not, I have a strong feeling that by this term the authors mean the algorithm does not converge to a resealable solution rather than being divergent up to a bandwidth. Maybe clarifying would be helpful.\n6) In section 5, the authors study the sensitivity to the hyperparameters. In this section, I had a hard time to understand the role of term 3\n\"BN and dropout hurts on-policy algorithms but can bring improvement only for the off-policy SAC algorithm.\" Does it mean that deploying BN, results in a more sensitive algorithm? or it means that the performance degrades (which is a different topic than section 5 is supposed to serve)?\n7) In section 7, the authors put out a hypothesis \"\nHowever, there is still generalization between samples: the agents are only trained on the limited\" but the provided empirical study might not fully be considered to be designed to test this hypothesis. In order to test this hypothesis, the author might be interested in training the models with bigger sample sizes, more training iteration, different function classes, and more fitting in order to test this hypothesis.\n\n3: Weak Reject", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 407 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper presents a comprehensive study of regularization techniques in deep reinforcement learning (RL), specifically focusing on policy optimization algorithms. The authors investigate the effectiveness of various regularization methods, including L2, L1, weight clipping, and batch normalization, on a range of continuous control tasks. The study finds that conventional regularization techniques, such as L2 regularization, can significantly improve agent performance on these tasks, particularly on more difficult ones. The paper also finds that entropy regularization, a commonly used regularization technique in RL, is not as effective as L2 regularization. The authors suggest that the lack of generalization gap between training and test environments in RL may contribute to the underutilization of regularization techniques in RL. The paper provides a valuable contribution to the field of RL by highlighting the importance of regularization techniques in policy optimization algorithms.\n\nMy japanese answer is:\n\n\u3053\u306e\u8ad6\u6587\u306f\u3001\u30c7\u30a3\u30fc\u30d7\u5f37\u5316\u5b66\u7fd2\uff08RL\uff09\u306b\u304a\u3051\u308b\u30dd\u30ea\u30b7\u30fc\u6700\u9069\u5316\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306b\u7279\u5316\u3057\u305f\u3001\u69d8\u3005\u306a\u6b63\u5247\u5316\u624b\u6cd5\u306e\u52b9\u679c\u3092\u8a73\u7d30\u306b\u8abf\u67fb\u3057\u3066\u3044\u307e\u3059\u3002\u8457\u8005\u305f\u3061\u306f\u3001L2\u3001L1\u3001\u30a6\u30a7\u30a4\u30c8\u30af\u30ea\u30c3\u30d4\u30f3\u30b0\u3001\u30d0\u30c3\u30c1\u30ce\u30eb\u30de\u30eb\u5316\u306a\u3069\u306e\u69d8\u3005\u306a\u6b63\u5247\u5316\u624b\u6cd5\u3092\u3001\u9023\u7d9a\u5236\u5fa1\u30bf\u30b9\u30af\u306e\u5e45\u5e83\u3044\u30bf\u30b9\u30af\u306b\u5bfe\u3057\u3066\u8abf\u67fb\u3057\u3066\u3044\u307e\u3059\u3002\u3053\u306e\u7814\u7a76\u306f\u3001L2\u6b63\u5247\u5316\u304c\u3053\u308c\u3089\u306e\u30bf\u30b9\u30af\u3067\u7279\u306b\u56f0\u96e3\u306a\u3082\u306e\u3067\u3042\u308b\u3053\u3068\u3092\u7279\u306b\u52b9\u679c\u7684\u3067\u3042\u308b\u3053\u3068\u3092\u898b\u3064\u3051\u307e\u3059\u3002\u307e\u305f\u3001RL\u3067\u4e00\u822c\u7684\u306b\u4f7f\u7528\u3055\u308c\u308b\u30a8\u30f3\u30c8\u30ed\u30d4\u30fc\u6b63\u5247\u5316\u306f\u3001L2\u6b63\u5247\u5316\u3088\u308a\u3082\u52b9\u679c\u7684\u3067\u306f\u306a\u3044\u3053\u3068\u3082\u660e\u3089\u304b\u306b\u306a\u3063\u3066\u3044\u307e\u3059\u3002\u8457\u8005\u305f\u3061\u306f\u3001RL\u3067\u306e\u6b63\u5247\u5316\u306e\u4e0d\u8db3\u304c\u3001\u4e00\u822c", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Recurrent Neural Networks have long been the dominating choice for sequence modeling. However, it severely suffers from two issues: impotent in capturing very long-term dependencies and unable to parallelize the sequential computation procedure. Therefore, many non-recurrent sequence models that are built on convolution and attention operations have been proposed recently. Notably, models with multi-head attention such as Transformer have demonstrated extreme effectiveness in capturing long-term dependencies in a variety of sequence modeling tasks. Despite their success, however, these models lack necessary components to model local structures in sequences and heavily rely on position embeddings that have limited effects and require a considerable amount of design efforts. In this paper, we propose the R-Transformer which enjoys the advantages of both RNNs and the multi-head attention mechanism while avoids their respective drawbacks. The proposed model can effectively capture both local structures and global longterm dependencies in sequences without any use of position embeddings. We evaluate R-Transformer through extensive experiments with data from a wide range of domains and the empirical results show that R-Transformer outperforms the state-of-the-art methods by a large margin in most of the tasks. We have made the code and data publicly available 1.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 195 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper proposes a new sequence learning model called R-Transformer, which combines the advantages of both Recurrent Neural Networks (RNNs) and the standard Transformer model. R-Transformer uses a local recurrent neural network (LocalRNN) to process signals within a local window ending at a given position, and the LocalRNN operates on local windows of all positions identically and independently, producing a latent representation for each of them. This allows the model to capture locality in the sequence while also incorporating global sequential information. The proposed model is evaluated on various sequence learning tasks and achieves stronger performance than both TCN and standard Transformer, as well as other state-of-the-art sequence models.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling , Authors: Shaojie Bai J Zico Kolter... , 2018\n2.  Title: Learning phrase representations using rnn encoder-decoder for statistical machine translation , Authors: Kyunghyun Cho Bart Van Merri\u00ebnboer... , 2014\n3.  Title: Language modeling with gated convolutional networks , Authors: Yann N Dauphin Angela Fan... , 2017\n4.  Title: A convolutional encoder model for neural machine translation , Authors: Jonas Gehring Michael Auli... , 2016\n5.  Title: Convolutional sequence to sequence learning , Authors: Jonas Gehring Michael Auli... , 2017\n6.  Title: Wavenet: A generative model for raw audio , Authors: A\u00e4ron Van Den Oord Sander Dieleman... , 2016\n7.  Title: Attention is all you need , Authors: Ashish Vaswani Noam Shazeer... , 2017\n", "gt": "Recurrent Neural Networks including its variants such LSTM (Hochreiter & Schmidhuber, 1997) and GRU (Cho et al., 2014) have long been the default choices for generic sequence modeling. A RNN sequentially processes each position in a sequence and maintains an internal hidden state to compresses information of positions that have been seen. While its design is appealing and it has been successfully applied in various tasks, several problems caused by its recursive structures including low computation efficiency and gradient exploding or vanishing make it ineffective when learning long sequences. Therefore, in recent years, a lot of efforts has been made to develop models\nwithout recursive structures and they can be roughly divided into two categories depending whether they rely on convolutions operations or not. The first category includes models that mainly built on convolution operations. For example, van den Oord et al. have designed an autoregressive WaveNet that is based on causal filters and dilated convolution to capture both global and local information in raw audios (Van Den Oord et al., 2016). Ghring et al. has successfully replace traditional RNN based encoder and decoder with convolutional ones and outperforms LSTM setup in neural machine translation tasks (Gehring et al., 2017; 2016). Moreover, researchers introduced gate mechanism into convolutions structures to model sequential dependencies in languages (Dauphin et al., 2017). Most recently, a generic architecture for sequence modeling, termed as Temporal Convolutional Networks (TCN), that combines components from previous works has been proposed in (Bai et al., 2018). Authors in (Bai et al., 2018) have systematically compared TCN with canonical recurrent networks in a wide range of tasks and TCN is able achieve better performance in most cases. Our R-transformer is motivated by works in this group in a sense that we firstly models local information and then focus on global ones. The most popular works in second category are those based on multi-head attention mechanism. The multi-head attention mechanism was firstly proposed in Vaswani et al.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 326 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper proposes a new sequence learning model called R-Transformer, which combines the advantages of both Recurrent Neural Networks (RNNs) and the Transformer model. R-Transformer addresses the limitations of RNNs, such as gradient vanishing and exploding problems, and the difficulty of parallelizing the computation in both the training and testing procedure. R-Transformer also improves upon the Transformer model by incorporating a local recurrent neural network (LocalRNN) that processes signals within a local window ending at a given position. This allows the model to capture locality in the sequence and incorporate global sequential information. The proposed model is evaluated on various sequence learning tasks and achieves stronger performance than both TCN and standard Transformer, as well as other state-of-the-art sequence models.\n\nThe related work section should discuss the following papers:\n\n1. \"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling\" by Shaojie Bai et al. (2018)\n2. \"Learning phrase representations using rnn encoder-decoder for statistical machine translation\" by Kyunghyun Cho et al. (2014)\n3. \"Language modeling with gated convolutional networks\" by Yann N Dauphin et al. (2017)\n4. \"A convolutional encoder model for neural machine translation\" by Jonas Gehring et al. (2016)\n5. \"Convolutional sequence to sequence learning\" by Jonas Gehring et al. (2017)\n6. \"Wavenet: A generative model for raw audio\" by A\u00e4ron Van Den Oord et al. (2016)\n7. \"Attention is all you need\" by Ashish Vaswani et al. (2017)\n\nThe related work section should briefly summarize each paper and explain how R-Transformer differs from and improves upon these models.", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "The authors propose an architecture that combines the practices of recurrent and feed-forward sequence models. However, I have major concerns regarding the novelty this paper, the various claims it makes, as well as its experiment setting.\n\nMajor issues/questions:\n1. The techniques proposed by this paper lack novelty. For instance, the entire section 3.2 is simply the original design of the multi-head self-attention by Vaswani et al. The major difference between R-Transformer and the original Transformer is the replacement of positional embedding with an RNN layer, but (in my opinion) the authors did not demonstrate sufficiently its effectiveness via ablative studies (see below). Moreover, some prior works have already exploited the locality structure in Transformers. For instance, [1] showed that a sparse, local Transformer can work extremely well and be very efficient (they achieved SOTA on large-scale char-level language modeling tasks).\n\n2. The experiments do not entirely convince me. \n    i) The authors use the \"same hidden size for R-Transformer and Transformer.\" But in fact, as the R-Transformer has one extra RNN/LSTM/GRU layer at every level of the network, the tests were carried out (in effect) using a larger model than the baselines. I think the authors should instead control the # of model parameters, especially since you are running only on small tasks with small-sized models.\n    ii) It is nice that the authors tested R-Transformer on a variety of tasks--- this is important. However, in no way do these number achieve the levels of the \"state-of-the-art\", which the authors claim at the end of Section 1 (e.g., [2] has better number on seqMNIST and character-level PTB, and the Transformer-XL actually achieves <55 perplexity on word-level PTB). Therefore, the numbers don't look particularly appealing to me. \n    iii) Lack of more challenging, or large-scale experiments.\n    iv) Lack of ablative study. \n\n3. The authors claim that the finite-window RNN captures local features. But doesn't that claim only applies to the first layer? Once the first layer multi-head attention mixes all input elements across the sequence, the \"local\" features fed into the second layer RNN will be, actually, **global** features? Doesn't that \"defeat\" the purpose of using a local RNN though?\n\n3: Weak Reject", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 359 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper proposes a new sequence learning model called R-Transformer, which combines the advantages of both Recurrent Neural Networks (RNNs) and the Transformer model. R-Transformer uses a local recurrent neural network (LocalRNN) to process signals within a local window ending at a given position, and a multi-head attention mechanism to capture global long-term dependencies. The LocalRNN operates on local windows of all positions identically and independently, and produces a latent representation for each of them. This allows the model to capture both local and global information in a sequence. The authors claim that R-Transformer achieves stronger performance than both Temporal Convolution Networks (TCN) and the standard Transformer, as well as other state-of-the-art sequence models.\n\nOverall, the paper presents a well-structured and well-written proposal for a new sequence learning model. The authors provide a clear motivation for their work, and describe the proposed model in detail. They also provide experimental results that demonstrate the effectiveness of R-Transformer in various sequence learning tasks.\n\nOne potential concern with the proposed model is that the LocalRNN may not be able to capture long-term dependencies as effectively as the multi-head attention mechanism in the standard Transformer. While the authors claim that the LocalRNN is able to capture local structures in a sequence, it is not clear how well it is able to capture long-term dependencies. Additionally, it is not clear how the LocalRNN is able to incorporate information from positions that are not within the local window.\n\nAnother potential concern is that the proposed model may be more complex and computationally expensive than other sequence learning models. While the authors claim that the LocalRNN can be implemented with full parallelization over the positions in a sequence, it is not clear how this would be implemented in practice. Additionally, the use of a local recurrent neural network may add additional computational complexity to the model.\n\nIn conclusion, the proposed R-Transformer model presents a novel approach to sequence learning that combines the advantages of both RNNs and the Transformer model. While the model may have some limitations, the authors provide experimental results that demonstrate its effectiveness in various sequence learning tasks. Overall, the paper presents a well-structured and well-written proposal for a new sequence learning model that is worth further investigation.", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "We present a simple and effective algorithm designed to address the covariate shift problem in imitation learning. It operates by training an ensemble of policies on the expert demonstration data, and using the variance of their predictions as a cost which is minimized with RL together with a supervised behavioral cloning cost. Unlike adversarial imitation methods, it uses a fixed reward function which is easy to optimize. We prove a regret bound for the algorithm which is linear in the time horizon multiplied by a coefficient which we show to be low for certain problems on which behavioral cloning fails. We evaluate our algorithm empirically across multiple pixel-based Atari environments and continuous control tasks, and show that it matches or significantly outperforms behavioral cloning and generative adversarial imitation learning.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 129 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper presents a new algorithm called DRIL (Disagreement-Regularized Imitation Learning) to address the problem of covariate shift in imitation learning settings. The algorithm trains an ensemble of policies on demonstration data and uses the disagreement in their predictions as a cost which is optimized through reinforcement learning together with a supervised behavioral cloning cost. The motivation is that the policies in the ensemble will tend to agree on the set of states covered by the expert, leading to low cost, but are more likely to disagree on states not covered by the expert, leading to high cost. The RL cost thus guides the agent back towards the distribution of the expert, while the supervised cost ensures that it mimics the expert within the expert\u2019s distribution. The algorithm is evaluated across multiple pixel-based Atari environments and continuous control tasks, and shows that it matches or significantly outperforms behavioral cloning and generative adversarial imitation learning, often recovering expert performance with only a few trajectories.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Learning to search better than your teacher , Authors: Kai-Wei Chang Akshay Krishnamurthy... , 2015\n2.  Title: Hierarchical imitation and reinforcement learning , Authors: Hoang M Le Nan Jiang... , 2018\n3.  Title: Learning self-correctable policies and value functions from demonstrations with negative sampling , Authors: Yuping Luo Huazhe Xu... , 2019\n4.  Title: Overcoming exploration in reinforcement learning with demonstrations , Authors: Ashvin Nair Bob McGrew... , 2018\n5.  Title: Alvinn: An autonomous land vehicle in a neural network , Authors: Dean A. Pomerleau , 1989\n6.  Title: SQIL: imitation learning via regularized behavioral cloning , Authors: Siddharth Reddy Anca D. Dragan... , 2019\n7.  Title: A reduction of imitation learning and structured prediction to no-regret online learning , Authors: Stephane Ross Geoffrey Gordon... , 2011\n8.  Title: Deeply aggrevated: Differentiable imitation learning for sequential prediction , Authors: Wen Sun Arun Venkatraman... , 2017\n9.  Title: Improving multi-step prediction of learned time series models , Authors: Arun Venkatraman Martial Hebert... , 2015\n", "gt": "The idea of learning through imitation dates back at least to the work of (Pomerleau, 1989), who trained a neural network to imitate the steering actions of a human driver using images as input. The problem of covariate shift was already observed, as the author notes: \u201cthe network must not solely be shown examples of accurate driving, but also how to recover once a mistake has been made\u201d. This issue was formalized in the work of (Ross & Bagnell, 2010), who on one hand proved an O( T 2) regret bound, and on the other hand provided an example showing this bound is tight. The subsequent work (Ross et al., 2011) proposed the DAGGER algorithm which obtains linear regret, provided the agent can both interact with the environment, and query the expert policy. Our approach also requires environment interaction, but importantly does not need to query the expert. note is the work of (Venkatraman et al., 2015), which extended DAGGER to time series prediction problems by using the true targets as expert corrections. Imitation learning has been used within the context of modern RL to help improve sample efficiency (Chang et al., 2015; Ross & Bagnell, 2014; Sun et al., 2017; Hester et al., 2018; Le et al., 2018; Cheng & Boots, 2018) or overcome exploration (Nair et al., 2017). These settings assume the reward is known and that the policies can then be fine-tuned with reinforcement learning. In this case, covariate shift is less of an issue since it can be corrected using the reinforcement signal. The work of (Luo et al., 2019) also proposed a method to address the covariate shift problem when learning from demonstrations when the reward is known, by conservatively extrapolating the value function outside the training distribution using negative sampling. This addresses a different setting from ours, and requires generating plausible states which are off the manifold of training data, which may be challenging when the states are high dimensional such as images. The work of (Reddy et al., 2019) proposed to treat imitation learning within the Q-learning framework, setting a positive reward for all transitions inside the demonstration data and zero reward for all other transitions in the replay buffer. This rewards the agent for repeating (or returning to) the expert\u2019s transitions.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 379 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper proposes a new and simple algorithm called DRIL (Disagreement-Regularized Imitation Learning) to address the covariate shift problem in imitation learning, in the setting where the agent is allowed to interact with its environment. The algorithm trains an ensemble of policies on the demonstration data, and uses the disagreement in their predictions as a cost which is optimized through RL together with a supervised behavioral cloning cost. The motivation is that the policies in the ensemble will tend to agree on the set of states covered by the expert, leading to low cost, but are more likely to disagree on states not covered by the expert, leading to high cost. The RL cost thus guides the agent back towards the distribution of the expert, while the supervised cost ensures that it mimics the expert within the expert\u2019s distribution. The paper evaluates DRIL empirically across multiple pixel-based Atari environments and continuous control tasks, and shows that it matches or significantly outperforms behavioral cloning and generative adversarial imitation learning, often recovering expert performance with only a few trajectories.\n\nThe paper is related to the following works:\n\n1. \"Learning to search better than your teacher\" (Chang et al., 2015) which proposes a method for learning to search better than the teacher by combining the teacher's knowledge with the student's own exploration.\n2. \"Hierarchical imitation and reinforcement learning\" (Le et al., 2018) which proposes a hierarchical imitation and reinforcement learning framework that can learn complex skills by combining low-level imitation learning with high-level reinforcement learning.\n3. \"Learning self-correctable policies and value functions from demonstrations with negative sampling\" (Luo et al., 2019) which proposes a method for learning self-correctable policies and value functions from demonstrations using negative sampling.\n4. \"Overcoming exploration in reinforcement learning with demonstrations\" (Nair et al., 2018) which proposes a method for overcoming exploration in reinforcement learning with demonstrations by using a demonstration-based exploration bonus.\n5. \"Alvinn: An autonomous land vehicle in a neural network\" (Pomerleau, 1", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "The paper proposes a simple but effective method to address the important issue of covariate shift. The method performs well empirically and has a theoretical support (although only for a tabular setting). While there are some issues (see below), this is a good paper. I vote for acceptance.  \n\n* Major comments and questions:\n- Accuracy of posterior approximation via ensemble. \nIt is unclear whether the posterior approximated from ensemble is accurate. More specifically, these ensemble policies are trained using BC loss. Under a limited amount of data (where BC fails), these policies would also fail and are inaccurate. Therefore, it should not be expected that a posterior from these inaccurate policies is accurate. Have the authors measure or analyze accuracy of these policies or that of the posterior? This important point is not mentioned or analyzed in the paper.\n\n- Alternative approaches to posterior approximation and uncertainty computation. \nThere are other approaches to obtain a posterior besides the ensemble approach, e.g., Bayesian neural networks. Such alternatives were not mentioned in the paper. Also, there are other quantities for measuring uncertainty besides the variance such as the entropy. These approaches and quantities have different pros and cons and they should be discussed in the paper.\n\n- Sample complexity in terms of environment interactions. \nThe sample complexity in terms of environment interactions is an important criterion for IL. I suggest the authors to include this criterion in the experiments. \n\n* Minor questions:\n- Why does the minibatch size is only 4 in the experiments for all methods. This is clearly too small for a reasonable training of deep networks. Is this a typo?\n\n- It is strange to not evaluate GAIL in the continuous control experiments, since GAIL was originally evaluated in these domains. I strongly suggest the authors to evaluate GAIL (and perhaps stronger methods such as VAIL (Peng et al., 2019)) in the continuous control experiments.\n\n8: Accept", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 318 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper proposes a new and simple algorithm called DRIL (Disagreement-Regularized Imitation Learning) to address the covariate shift problem in imitation learning. The algorithm trains an ensemble of policies on the demonstration data and uses the disagreement in their predictions as a cost which is optimized through RL together with a supervised behavioral cloning cost. The motivation is that the policies in the ensemble will tend to agree on the set of states covered by the expert, leading to low cost, but are more likely to disagree on states not covered by the expert, leading to high cost. The RL cost thus guides the agent back towards the distribution of the expert, while the supervised cost ensures that it mimics the expert within the expert\u2019s distribution. The authors evaluate DRIL empirically across multiple pixel-based Atari environments and continuous control tasks, and show that it matches or significantly outperforms behavioral cloning and generative adversarial imitation learning, often recovering expert performance with only a few trajectories.\n\nIn conclusion, the paper presents a new algorithm that addresses the problem of covariate shift in imitation learning. The algorithm is simple and effective, and it outperforms existing methods in several environments. The authors provide a clear explanation of the algorithm and its theoretical foundations, and they support their claims with experimental evidence.\n\nOne suggestion for future work would be to apply the algorithm to more complex environments and tasks, and to compare its performance with other state-of-the-art methods. Another question that arises is whether the algorithm can be adapted to other types of imitation learning, such as imitation from demonstrations with a human teacher.\n\nOverall, the paper presents a significant contribution to the field of imitation learning, and it provides a useful tool for researchers and practitioners working in this area.", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Deterministic neural networks (NNs) are increasingly being deployed in safety critical domains, where calibrated, robust and efficient measures of uncertainty are crucial. While it is possible to train regression networks to output the parameters of a probability distribution by maximizing a Gaussian likelihood function, the resulting model remains oblivious to the underlying confidence of its predictions. In this paper, we propose a novel method for training deterministic NNs to not only estimate the desired target but also the associated evidence in support of that target. We accomplish this by placing evidential priors over our original Gaussian likelihood function and training our NN to infer the hyperparameters of our evidential distribution. We impose priors during training such that the model is penalized when its predicted evidence is not aligned with the correct output. Thus the model estimates not only the probabilistic mean and variance of our target but also the underlying uncertainty associated with each of those parameters. We observe that our evidential regression method learns well-calibrated measures of uncertainty on various benchmarks, scales to complex computer vision tasks, and is robust to adversarial input perturbations.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 185 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper presents a novel approach for estimating the evidence in support of network predictions by directly learning both the inferred aleatoric uncertainty as well as the underlying epistemic uncertainty over its predictions. The approach is based on using a Gaussian process to model the data and placing a conjugate prior distribution on the mean and variance of the data. The prior is chosen to be a Gaussian distribution with a mean of zero and a variance of \u03c32. The authors then use maximum likelihood estimation to learn the parameters of the prior distribution, which maximizes the likelihood of observing the data given the prior. The resulting posterior distribution is then used to make predictions on new data. The approach is evaluated on several synthetic and real-world datasets and is shown to outperform other methods for predictive uncertainty estimation.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Mixture density networks , Authors: Christopher M Bishop , 1994\n2.  Title: Weight uncertainty in neural networks , Authors: Charles Blundell Julien Cornebise... , 2015\n3.  Title: A weakly informative default prior distribution for logistic and other regression models , Authors: Andrew Gelman Aleks Jakulin... , 2008\n4.  Title: Prior distributions for variance parameters in hierarchical models (comment on article by browne and draper) , Authors: Andrew Gelman , 2006\n5.  Title: Variational dropout and the local reparameterization trick , Authors: Durk P Kingma Tim Salimans... , 2015\n6.  Title: Simple and scalable predictive uncertainty estimation using deep ensembles , Authors: Balaji Lakshminarayanan Alexander Pritzel... , 2017\n7.  Title: Variational dropout sparsifies deep neural networks , Authors: Dmitry Molchanov Arsenii Ashukha... , 2017\n8.  Title: Evidential deep learning to quantify classification uncertainty , Authors: Murat Sensoy Lance Kaplan... , 2018\n", "gt": "Uncertainty estimation has a long history in neural networks, from modeling probability distribution parameters over outputs (Bishop, 1994) to Bayesian deep learning (Kendall & Gal, 2017). Our work builds on this foundation and presents a scalable representation for inferring the parameters of an evidential uncertainty distribution while simultaneously learning regression tasks via MLE. In Bayesian deep learning, priors are placed over network weights and estimated using variational inference (Kingma et al., 2015). Dropout (Gal & Ghahramani, 2016; Molchanov et al., 2017) and BBB (Blundell et al., 2015) rely on multiple samples to estimate predictive variance. Ensembles (Lakshminarayanan et al., 2017) provide a tangential approach where sampling occurs over multiple trained instances. In contrast, we place uncertainty priors over the likelihood function and thus only need a single forward pass to evaluate both prediction and uncertainty. Additionally, our approach of uncertainty estimation proved to be better calibrated and capable of predicting where the model fails. A large topic of research in Bayesian inference focuses on placing prior distributions over hierarchical models to estimate uncertainty (Gelman et al., 2006; 2008). Our methodology falls under the class of evidential deep learning which models higher-order distribution priors over neural network predictions to interpret uncertainty. Prior works in this field (Sensoy et al., 2018; Malinin & Gales, 2018) have focused exclusively on modeling uncertainty in the classification domain with Dirichlet prior distributions. Our work extends this field into the broad range of regression learning tasks (e.g. depth estimation, forecasting, robotic control learning, etc.) and demonstrates generalizability to out-of-distribution test samples and complex learning problems.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 259 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper presents a novel approach for estimating the evidence in support of network predictions by directly learning both the inferred aleatoric uncertainty as well as the underlying epistemic uncertainty over its predictions. The approach is based on using a Gaussian process to model the data and placing a conjugate prior distribution on the unknown mean and variance of the data. The paper then describes a method for learning the hyperparameters of this distribution, given an input. The method is based on using a neural network to infer the hyperparameters of the higher-order, evidential distribution, given an input. The approach presents several advantages over prior work, including the ability to simultaneously learn the desired regression task, along with aleatoric and epistemic uncertainty estimation, built in, by enforcing evidential priors. The paper also describes a regularization loss, which is used to enforce a prior to inflate the uncertainty estimates. The paper evaluates the performance of the approach on a one-dimensional toy regression dataset and compares it to several benchmarks, including deterministic regression, MC-dropout, model-ensembles, and Bayes-by-Backprop. The results show that the approach is able to estimate uncertainty appropriately and grows the uncertainty estimate with increasing distance from the training data.", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "This paper proposes a novel approach to estimate the confidence of predictions in a regression setting. The approach starts from the standard modelling assuming iid samples from a Gaussian distribution with unknown mean and variances and places evidential priors. This opens the door to online applications with fully integrated uncertainty estimates. \n\nPros:\n1.\tNovel approach to regression (a similar work has been published at NeurIPS last year for classification [3]), but the extension of the work to regression is important.\n2.\tThe experimental results show consistent improvement in performance over a wide base of benchmarks, scales to large vision problems and behaves robustly against adversarial examples.\n3.\tThe presentation of the paper is overall nice, and the Figures are very useful to the general comprehension of the article.\nCons:\n1.\tThe theory of evidence, which is not widely known in the ML community, is not clearly introduced. \nI think that the authors should consider adding a section similar to Section 3 of Sensoy et al. [3] should be considered. Currently, the only step explaining the evidential approach that I found was in section 3.1, in a very small paragraph (between \u201cthe mean of [\u2026] to \\lambda + 2\\alpha.\u201d). I believe that the article would greatly benefit from a more thorough introduction of concepts linked to the theory of evidence.\n2.\tThe authors briefly mention that KL is not well defined between some NIG distributions (p.5) and propose a custom evidence regularizer, but there\u2019s very little insight given on how this connects to/departs from the ELBO approach. \n\nOther comments/questions:\n1.\t(p.1)  I\u2019m not sure to fully understand what\u2019s meant by higher-order/lower-order distributions, could you clarify?\n2.\t(p.3) In section 3.1, the term in the total evidence \\phi_j is not defined.\n3.\t(p.3) Could you comment on the implications of assuming that the estimated distribution can be factorized? \n4.\t(p.4) Could you comment on the difference that there is between NLL_ML and NLL_SOS from a modelling perspective?\n5.\t(p.4) The ELBO loss (6) is unclearly defined, and not connected to the direct context. I would suggest moving this to the section 3.3, where the prior p(\\theta) used in eq. (6) is actually defined.\n\nSpelling & typos:\n-\t(p.4) There are several typos in equation (8), where tau should be replaced with 1/\\sigma^2. \n-\t(p.8) In the last sentence, there is \u201cntwork\u201d instead of network.\n-\t(p.9) There is a typo in the name of J\u00f8sang in the references. \n-\t(p.10) In equation (13), due to the change of variable, there should be a \n-(1/\\tau^2) added;  \n-\t(p.10) In equation (14), the \\exp(-\\lambda*\\pi*(\u2026)) should be replaced with \\exp(-\\lambda*\\tau*(\u2026)). \n\n6: Weak Accept", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 438 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper presents a novel approach for estimating the evidence in support of network predictions by directly learning both the inferred aleatoric uncertainty as well as the underlying epistemic uncertainty over its predictions. The approach is based on using a Gaussian process to model the data and placing a conjugate prior distribution on the unknown mean and variance of the data. The paper then describes a method for learning the hyperparameters of this prior distribution, given an input. The method is based on maximizing the model evidence, which is the likelihood of an observation given the prior parameters, and minimizing the evidence or inflating uncertainty when the prediction is wrong. The paper also describes a regularization loss, which is used to enforce a prior to inflate the uncertainty estimates. The method is evaluated on a one-dimensional toy regression dataset and on several real-world datasets, and is shown to outperform other state-of-the-art methods for predictive uncertainty estimation.\n\nIn general, the paper presents a well-motivated and well-defined approach for estimating uncertainty in the predictions of a neural network. The use of a Gaussian process to model the data and a conjugate prior distribution to model the uncertainty is a natural and intuitive choice, and the method for learning the hyperparameters of the prior is well-justified. The regularization loss is also a useful addition, as it allows for the enforcement of a prior on the uncertainty estimates.\n\nOne question that arises is how well the method generalizes to more complex datasets and tasks. While the paper shows good performance on the datasets considered, it is not clear how the method would perform on more challenging tasks, such as those with more complex data or more varied predictions. It would also be interesting to see how the method compares to other approaches for uncertainty estimation, such as Bayesian neural networks or Monte Carlo dropout.\n\nAnother question is how the method can be extended to handle more complex uncertainty models, such as those that allow for non-Gaussian distributions or correlations between data points. While the current approach is well-suited for modeling uncertainty in the form of Gaussian noise, it is not clear how it can be extended to handle more complex uncertainty models.\n\nOverall, the paper presents a promising approach for estimating uncertainty in the predictions of a neural network, and provides a useful contribution to the field of predictive uncertainty estimation.", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Answerer in Questioner\u2019s Mind (AQM) is an information-theoretic framework that has been recently proposed for task-oriented dialog systems. AQM benefits from asking a question that would maximize the information gain when it is asked. However, due to its intrinsic nature of explicitly calculating the information gain, AQM has a limitation when the solution space is very large. To address this, we propose AQM+ that can deal with a large-scale problem and ask a question that is more coherent to the current context of the dialog. We evaluate our method on GuessWhich, a challenging task-oriented visual dialog problem, where the number of candidate classes is near 10K. Our experimental results and ablation studies show that AQM+ outperforms the state-of-the-art models by a remarkable margin with a reasonable approximation. In particular, the proposed AQM+ reduces more than 60% of error as the dialog proceeds, while the comparative algorithms diminish the error by less than 6%. Based on our results, we argue that AQM+ is a general task-oriented dialog algorithm that can be applied for non-yes-or-no responses.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 174 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper proposes a new algorithm called AQM+, which is an extension of the AQM algorithm. AQM+ is designed to handle more complex and large-scale task-oriented dialog problems. The algorithm generates candidate questions and answers at every turn and selects one to ask a question. This approach allows the algorithm to generate a more contextual question. The authors conducted extensive experiments on the GuessWhich task-oriented visual dialog task and demonstrated that AQM+ outperformed comparative deep learning models by a large margin. The authors also conducted ablation studies to show that their information gain approximation is reasonable. The experimental results provide insights on how the AQM framework can further improve on top of the SL and RL approaches. The main contributions of the paper are summarized as a more generalized version of AQM, which can handle more complicated problems with a larger number of candidate classes, and a more contextual question generation approach.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Learning cooperative visual dialog agents with deep reinforcement learning , Authors: Abhishek Das Satwik Kottur... , 2017\n2.  Title: Guesswhat?! visual object discovery through multi-modal dialogue , Authors: Harm de Vries Florian Strub... , 2017\n3.  Title: Criteria for human-compatible ai in two-player vision-language tasks , Authors: Cheolho Han Sang-Woo Lee... , 2017\n4.  Title: Two can play this game: Visual dialog with discriminative question generation and answering , Authors: Unnat Jain Svetlana Lazebnik... , 2018\n5.  Title: Nsml: Meet the mlaas platform with a real-world case study , Authors: Hanjoo Kim Minkyu Kim... , 2018\n6.  Title: Codraw: Visual dialog for collaborative drawing , Authors: Jin-Hwa Kim Devi Parikh... , 2017\n7.  Title: Answerer in questioner\u2019s mind for goal-oriented visual dialogue , Authors: Sang-Woo Lee Yu-Jung Heo... , 2018\n8.  Title: Microsoft coco: Common objects in context , Authors: Tsung-Yi Lin Michael Maire... , 2014\n9.  Title: Best of both worlds: Transferring knowledge from discriminative learning to a generative visual dialog model , Authors: Jiasen Lu Anitha Kannan... , 2017\n10.  Title: Query-reduction networks for question answering , Authors: Minjoon Seo Sewon Min... , 2017\n11.  Title: Neural speed reading via skim-rnn , Authors: Minjoon Seo Sewon Min... , 2018\n12.  Title: Visual reference resolution using attention memory for visual dialog , Authors: Paul Hongsuck Seo Andreas Lehrmann... , 2017\n13.  Title: Multimodal hierarchical reinforcement learning policy for task-oriented visual dialog , Authors: Jiaping Zhang Tiancheng Zhao... , 2018\n14.  Title: Goal-oriented visual question generation via intermediate rewards , Authors: Junjie Zhang Qi Wu... , 2018\n", "gt": "A task-oriented visual dialog problem has recently been paid attention in the field of computer vision and natural language processing (Kim et al., 2017). GuessWhat is one of the famous taskoriented dialog tasks, where the goal is to figure out a target object in the image through a dialog that the answerer has in mind (de Vries et al., 2017). However, GuessWhat is relatively an easy task because it only allows the answer form of yes or no. The baseline visual question answering (VQA) model achieves 78.5%. In the object guessing task (i.e., GuessWhat task itself), the state-of-the-art averaged accuracy of SL, RL (Zhang et al., 2018b), and AQM (Lee et al., 2018) reached 44.6% and 60.8%, and 72.9% at the 5th round, respectively. Random guessing baseline has an accuracy of\n16.0% (Han et al., 2017), thus RL algorithms achieve 53.3% error decrease, whereas AQM achieves 67.7%. GuessWhich is a cooperative two-player game that one player tries to figure out an image out of 9,628 that another has in mind (Das et al., 2017b). GuessWhich uses Visual Dialog dataset (Das et al., 2017a) which includes human dialogs on MSCOCO images (Lin et al., 2014) as well as the captions that are generated. Although GuessWhich is similar to GuessWhat, it is more challenging in every task including asking a question, giving an answer, and guessing the target class. For example, unlike GuessWhat that can be answered in yes or no, the answer can be an arbitrary sentence in GuessWhich. Therefore, the VQA task in the Visual Dialog dataset is much studied than the GuessWhat dataset (Lu et al., 2017; Seo et al., 2017b). Similar to GuessWhat, SL and RL approaches have been applied to solve the GuessWhich task and they showed a moderate increase in performance (Das et al., 2017b; Jain et al., 2018; Zhang et al., 2018a). However, based on the authors\u2019 recent Github implementation1 of the papers in ICCV (Das et al., 2017b), SL and RL methods have shown that only 6% of error is diminished through the dialog compared to the zeroth turn baselines which only use generated caption.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 351 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper proposes a new algorithm called AQM+ for task-oriented dialog problems. AQM+ is an end-to-end neural approach that uses sequence-to-sequence models and reinforcement learning to generate appropriate sentences for the task. AQM+ treats the problem as a series of games of twenty questions and selects the question that gives the maximum information gain. The algorithm is able to handle a more complicated problem where the number of candidate classes is extremely large. AQM+ generates candidate questions and answers at every turn, and then selects one of them to ask a question. The algorithm considers the previous history of the dialog, which makes it more contextual. The proposed algorithm outperforms comparative deep learning models by a large margin in GuessWhich, a challenging task-oriented visual dialog task.", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "The paper proposes an improvement over the AQM approach for an information-theoretic framework for task-oriented dialog systems. Specifically, the paper tries to circumvent the problem of explicitly calculating the information gain while asking a question in the AQM setting. While the original AQM approach sweeps over all possible guesses and answers while estimating information gain, this is rendered impractical in scenarios where this space cannot be tractably enumerated. As a solution, AQM+ proposes sweeping over only some top-k relevant instantiations of answers and guesses in this space by normalizing the probabilities of the subset of the space in consideration. \n\nComments:\n- The paper is overloaded with notations and the writing is not very smooth. The terse nature of the content makes it hard to follow in general. If someone apriori was not familiar with task-oriented dialog or the visual dialog setting in Das et al. (2017b), it would be quite hard to follow.\n- While mentioning SL/RL approaches while comparing or introducing the setup, the authors do not make any distinction between discriminative and generative dialog models. Specifically, SL approaches could either be trained discriminatively to rank options among the provided ones given dialog context or in a generative manner via token-level teacher forcing. The authors should clearly make this distinction in the introduction and in other places where it\u2019s needed.\n- The authors should stress more upon the approximations involved while calculating mutual information. As far as I understand, even in the AQM approach the numerator and the denominator within the logarithm are estimated from a different set of parameters and as such they need not be consistent with each other under marginalization. The term resembles MI and ensuring consistency in such a framework would require either of the numerator or the denominator to be close to something like a variational approximation of the true distribution. In addition, AQM+ adopts the same framework as AQM but computes MI over some top-k of the random variables being considered. Could the authors comment more on why restricting the space of r.v.\u2019s to some top-k samples is a good idea? Would that not lead to somewhat of a biased estimator?\n- Unless I am missing something, training aprxAgen from the training data (indA) seems odd. Assuming, this to be Qbot\u2019s mental model of Abot -- there is no prior reason why this should be initialized or trained in such a manner. Similarly, the training paradigm of the depA setting is confusing. If they are trained in a manner similar to a regular Abot -- either SL or RL -- then they\u2019re not approximate mental models but are rather just another Abot agent in play which is being queried by\n\n6: Marginally above acceptance threshold", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 452 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper proposes a new algorithm called AQM+ for the task-oriented visual dialog problem. AQM+ is an extension of the AQM algorithm, which treats the problem as a series of twenty question games and selects the question that gives the maximum information gain. AQM+ generates candidate questions and answers at every turn and selects one of them to ask a question. The algorithm considers the previous history of the dialog, which makes it more contextual. The proposed algorithm outperforms comparative deep learning models by a large margin in the GuessWhich task-oriented visual dialog task. The authors also propose a more generalized version of AQM+ that can handle a more complicated problem where the number of candidate classes is extremely large. The authors also suggest that AQM+ can be used in other task-oriented dialog tasks.\n\nIn conclusion, the paper presents a new algorithm for the task-oriented visual dialog problem that outperforms current state-of-the-art methods. The proposed algorithm is more contextual and can handle a more complicated problem. The authors also suggest that AQM+ can be used in other task-oriented dialog tasks. However, the paper does not provide a detailed comparison with other non-deep learning methods, and it would be interesting to see how AQM+ compares to these methods. Additionally, the paper does not provide a detailed analysis of the impact of the different hyperparameters on the performance of the algorithm. It would be interesting to see a more in-depth analysis of the impact of these hyperparameters on the performance of the algorithm.", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Recent studies have shown that reinforcement learning (RL) models can be vulnerable in various scenarios, where noises from different sources could appear. For instance, the observed reward channel is often subject to noise in practice (e.g., when observed rewards are collected through sensors), and thus observed rewards may not be credible. Also, in applications such as robotics, a deep reinforcement learning (DRL) algorithm can be manipulated to produce arbitrary errors. In this paper, we consider noisy RL problems where observed rewards by RL agents are generated with a reward confusion matrix. We call such observed rewards as perturbed rewards. We develop an unbiased reward estimator aided robust RL framework that enables RL agents to learn in noisy environments while observing only perturbed rewards. Our framework draws upon approaches for supervised learning with noisy data. The core ideas of our solution include estimating a reward confusion matrix and defining a set of unbiased surrogate rewards. We prove the convergence and sample complexity of our approach. Extensive experiments on different DRL platforms show that policies based on our estimated surrogate reward can achieve higher expected rewards, and converge faster than existing baselines. For instance, the state-of-the-art PPO algorithm is able to obtain 67.5% and 46.7% improvements in average on five Atari games, when the error rates are 10% and 30% respectively.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 219 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nIn this document, the authors present a method for building reinforcement learning models that can handle noisy rewards. They propose a solution that uses a reward function that produces credible rewards in the presence of noise. The method is based on a reward confusion matrix, which generates observed rewards that are subject to randomness. The authors propose an efficient and flexible estimation module that allows the method to handle a wide range of noise rates. The proposed method is tested on a variety of reinforcement learning problems, including cartpole, pendulum, and atari games. The results show that the proposed method is able to achieve similar or better performance than the baseline methods, even when the noise rates are high. The authors also provide an analysis of the convergence and sample complexity for the policy trained using their proposed method.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Learning robust rewards with adversarial inverse reinforcement learning , Authors: Justin Fu Katie Luo... , 2017\n2.  Title: Continuous deep q-learning with model-based acceleration , Authors: Shixiang Gu Timothy P. Lillicrap... , 2016\n3.  Title: Adversary a3c for robust reinforcement learning, 2018 , Authors: Zhaoyuan Gu Zhenzhong Jia... , 2018\n4.  Title: Adversarial attacks on neural network policies , Authors: Sandy Huang Nicolas Papernot... , 2017\n5.  Title: Deep reinforcement learning doesn\u2019t work yet , Authors: Alex Irpan , 2018\n6.  Title: Reinforcement learning in robust markov decision processes , Authors: Shiau Hong Lim Huan Xu... , 2016\n7.  Title: Tactics of adversarial attack on deep reinforcement learning agents , Authors: Yen-Chen Lin Zhang-Wei Hong... , 2017\n8.  Title: Learning from corrupted binary labels via class-probability estimation , Authors: Aditya Menon Brendan Van Rooyen... , 2015\n9.  Title: Learning with noisy labels , Authors: Nagarajan Natarajan Inderjit S Dhillon... , 2013\n10.  Title: Robust adversarial reinforcement learning , Authors: Lerrel Pinto James Davidson... , 2017\n11.  Title: Epopt: Learning robust neural network policies using model ensembles , Authors: Aravind Rajeswaran Sarvjeet Ghotra... , 2016\n12.  Title: Reinforcement learning under model mismatch , Authors: Aurko Roy Huan Xu... , 2017\n13.  Title: A rate of convergence for mixture proportion estimation, with application to learning from noisy labels , Authors: Clayton Scott , 2015\n14.  Title: Classification with asymmetric label noise: Consistency and maximal denoising , Authors: Clayton Scott Gilles Blanchard... , 2013\n15.  Title: Distral: Robust multitask reinforcement learning , Authors: Yee Whye Teh Victor Bapst... , 2017\n", "gt": "Robust Reinforcement Learning It is known that RL algorithms are vulnerable to noisy environments (Irpan, 2018). Recent studies (Huang et al., 2017; Kos & Song, 2017; Lin et al., 2017) show that learned RL policies can be easily misled with small perturbations in observations. The presence of noise is very common in real-world environments, especially in robotics-relevant applications. Consequently, robust (adversarial) reinforcement learning (RRL/RARL) algorithms have been widely studied, aiming to train a robust policy which is capable of withstanding perturbed observations (Teh et al., 2017; Pinto et al., 2017; Gu et al., 2018) or transferring to unseen environments (Rajeswaran et al., 2016; Fu et al., 2017). However, these robust RL algorithms mainly focus on noisy vision observations, instead of the observed rewards. A couple of recent works (Lim et al., 2016; Roy et al., 2017) have also looked into a rather parallel question of training robust RL algorithms with uncertainty in models. Learning with Noisy Data Learning appropriately with biased data has received quite a bit of attention in recent machine learning studies Natarajan et al. (2013); Scott et al. (2013); Scott (2015); Sukhbaatar & Fergus (2014); van Rooyen & Williamson (2015); Menon et al. (2015). The idea of above line of works is to define unbiased surrogate loss function to recover the true loss using the knowledge of the noises. We adapt these approaches to reinforcement learning. Though intuitively the idea should apply in our RL settings, our work is the first one to formally establish this extension both theoretically and empirically. Our quantitative understandings will provide practical insights when implementing reinforcement learning algorithms in noisy environments.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 269 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper presents a method for building reinforcement learning models that can handle noisy rewards. The proposed method is based on using a reward function that generates rewards according to a reward confusion matrix, which is a matrix that describes the probability of a given reward being corrupted by noise. The authors propose an algorithm for estimating the reward confusion matrix, and show that it is possible to learn a policy that is robust to the noise using this algorithm. The algorithm is based on Q-learning, and the authors show that it is possible to learn a policy that is robust to the noise using this algorithm. The authors also show that their method is able to learn a policy that is robust to the noise, and that it is able to learn a policy that is able to achieve the same performance as a policy that is trained using the true rewards.\n\nThe related work section should include the following papers:\n\n1. \"Learning robust rewards with adversarial inverse reinforcement learning\" by Justin Fu, Katie Luo, and others (2017)\n2. \"Continuous deep q-learning with model-based acceleration\" by Shixiang Gu, Timothy P. Lillicrap, and others (2016)\n3. \"Adversary a3c for robust reinforcement learning\" by Zhaoyuan Gu, Zhenzhong Jia, and others (2018)\n4. \"Adversarial attacks on neural network policies\" by Sandy Huang, Nicolas Papernot, and others (2017)\n5. \"Deep reinforcement learning doesn\u2019t work yet\" by Alex Irpan (2018)\n6. \"Reinforcement learning in robust markov decision processes\" by Shiau Hong Lim, Huan Xu, and others (2016)\n7. \"Tactics of adversarial attack on deep reinforcement learning agents\" by Yen-Chen Lin, Zhang-Wei Hong, and others (2017)\n8. \"Learning from corrupted binary labels via class-probability estimation\" by Aditya Menon, Brendan Van Rooyen, and others (2015)\n9. \"Learning with noisy labels\" by Nagarajan Natarajan, Inderjit S Dhillon, and", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "the paper focuses on an interesting and relatively unexplored direction for RL. Apart from the work cited by the paper (and perhaps work like Krueger et al. (2016), in which agents must pay some cost to observe true rewards), there is little work on learning settings of this kind. This paper represents a first step in gaining clarity on how to formalize and study this problem. I did, however, find the analysis and the experiments to be relatively disjointed -- the main sample complexity result presented by the paper (Theorem 2) was given for Phased Q-Learning, yet no experiments actually evaluate the performance of Phased Q-Learning. I think the paper could benefit from experiments focused on simple domains that showcase how traditional algorithms do in cases where it is easier to understand (and visualize) the impact of the reward perturbations (simple chain MDPs, grid worlds, etc.) -- and specifically experiments including Phased Q-Learning. \n\nSection 1) Introduction:\n\t- \"adversaries in real-world\" --> \"adversaries in the real-world\"\n\t- You might consider citing Loftin et al. (2014) regarding the bulleted point about \"Application-Specific Noise\".\n\t- \"unbiased reward estimator aided reward robust reinforcement learning framework\" --> this was a bit hard to parse. Consider making more concise, like: \"unbiased reward estimator for use in reinforcement learning with perturbed rewards\".\n\t- \"Our solution framework builds on existing reinforcement learning algorithms, including the recently developed DRL ones\" --> cite these up front So, cite: Q-Learning, CEM, SARSA, DQN, Dueling DQN, DDPG, NAF, and PPO, and spell out the acronym for each the first time you introduce them.\n\t- \"layer of explorations\" --> \"layer of exploration\"\n\nSection 2) Problem Formulation\n\t- \"as each shot of our\" --> what is 'shot' in this context?\n\t- \"In what follow,\" --> \"In what follows,\"\n\t- \"where 0 < \\gamma \\leq 1\" --> Usually, $\\gamma \\in [0,1)$, or $[0,1]$. Why can't $\\gamma = 0$?\n\t- The transition notation changes between $\\mathbb{P}_a(s_{t+1} | s_t)$ and $\\mathbb{P}(s_{t+1} | s_t, a_t)$. I'd suggest picking one and sticking with it to improve clarity.\n\t- \"to learn a state-action value function, for example the Q-function\" --> Why is the Q-function just an example? Isn't is *the* state-action value function? That is, I'd suggest replacing \"to learn a state-action value function, for example the Q-function\" with \"to learn a state-action value function, also called the Q-function\"\n\t- \"Q-function calculates\" --> \"The Q-function denotes\"\n\t- \"the reward feedbacks perfectly\" --> \"the reward feedback perfectly\"\n\t- I prefer that the exposition of the perturbed reward MDP be done with C in the tuple. So: $\\tilde{M} = \\langle \\mathcal{S}, \\mathcal{A}, \\mathcal{R}, C, \\mathcal{P}, \\gamma \\rangle$. This seems the most appropriate definition, since the observed rewards will be generated by $C$.\n\t- The setup of the confusion matrix for reward noise over is very clean. It might be worth pointing out that $C$ need not be Markovian. There are cases where C is not just a function of $\\mathcal{S}$ and $\\mathcal{R}$, like the adversarial case you describe early on.\n\n\n4) Experiments:\n\t- Diverse experiments! That's great. Lots of algorithms, lots of environment types.\n\t- I expected to see Phrased Q-Learning in the experiments. Why was it not included?\n\t- The plots are pretty jagged, so I'm left feeling a bit skeptical about some of the results. The results would be strengthened if the experiments were repeated for more trials.\n\n6: Marginally above acceptance threshold", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 563 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nIn this document, the authors present a method for building reinforcement learning models that can handle noisy rewards. They propose a solution that involves using a reward function that produces credible rewards in the presence of noise. The method is designed to work with a specific type of noisy reward model, where the observed rewards are generated according to a reward confusion matrix. The authors provide a detailed analysis of the problem of learning from noisy rewards in reinforcement learning, and they propose a solution that is based on using an unbiased estimator for the true rewards. They also provide experimental results that demonstrate the effectiveness of their method.\n\nThe main contribution of this paper is the development of a method for building reinforcement learning models that can handle noisy rewards. The authors propose a solution that is based on using an unbiased estimator for the true rewards, and they provide experimental results that demonstrate the effectiveness of their method. The paper also provides a detailed analysis of the problem of learning from noisy rewards in reinforcement learning.\n\nOne potential application of this method is in the field of robotics, where sensors on a robot may be affected by physical conditions such as temperature and lighting. By using the proposed method, a reinforcement learning model could be trained to operate the robot in a noisy environment, such as a factory floor. Another potential application is in the field of online advertising, where the rewards for an advertising campaign may be noisy due to factors such as fraudulent clicks. By using the proposed method, a reinforcement learning model could be trained to optimize the advertising campaign in the presence of noisy rewards.\n\nOne potential limitation of the proposed method is that it requires a known reward confusion matrix, which may not be available in some cases. Additionally, the method may not be effective in cases where the noise is too high, or where the reward confusion matrix is too complex. Future work could focus on developing methods for estimating the reward confusion matrix in cases where it is not known, or for handling cases where the noise is too high.\n\nOverall, this paper makes a significant contribution to the field of reinforcement learning by proposing a method for handling noisy rewards. The proposed method has the potential to be useful in a variety of applications, and the experimental results demonstrate its effectiveness. However, there are also potential limitations that could be addressed in future", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Open-domain question answering remains a challenging task as it requires models that are capable of understanding questions and answers, collecting useful information, and reasoning over evidence. Previous work typically formulates this task as a reading comprehension or entailment problem given evidence retrieved from search engines. However, existing techniques struggle to retrieve indirectly related evidence when no directly related evidence is provided, especially for complex questions where it is hard to parse precisely what the question asks. In this paper we propose a retriever-reader model that learns to attend on essential terms during the question answering process. We build (1) an essential term selector which first identifies the most important words in a question, then reformulates the query and searches for related evidence; and (2) an enhanced reader that distinguishes between essential terms and distracting words to predict the answer. We evaluate our model on multiple open-domain QA datasets where it outperforms the existing state-of-the-art, notably leading to a relative improvement of 8.1% on the AI2 Reasoning Challenge (ARC) dataset.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 169 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nOpen-domain question answering (QA) has been widely studied in recent years. However, it becomes more difficult when dealing with questions with little evidence, free-form answer types, or complex questions requiring reasoning or commonsense knowledge. The authors propose an essential-term-aware Retriever-Reader (ET-RR) model that learns to attend to essential terms during retrieval and reading. The model consists of an essential term selector followed by an attention-enhanced reader. The essential term selector uses a recurrent neural network to select essential terms from the question, which are then used to formulate a more efficient query for the retriever. The retrieved evidence is then fed to the reader to predict the final answer. The reader model consists of an input layer, attention layer, sequence modeling layer, fusion layer, and output layer. The attention and fusion layers help the model to obtain a refined representation of one text sequence based on the understanding of another. The authors evaluate their model on the ARC dataset, achieving an accuracy of 36.61%, ranking first on the official leaderboard. They also adapt two datasets to the open-domain setting, RACE-Open and MCScript-Open, where they outperform baseline models by a large margin. Ablation studies show that each of the model's components contributes to its accuracy.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Think you have solved question answering? try arc, the ai2 reasoning challenge , Authors: Peter Clark Isaac Cowhey... , 2018\n2.  Title: Efficient and robust question answering from minimal context over documents , Authors: Sewon Min Victor Zhong... , 2018\n3.  Title: Retrieve-and-read: Multi-task learning of information retrieval and reading , Authors: Kyosuke Nishida Itsumi Saito... , 2018\n4.  Title: Yuanfudao at semeval-2018 task 11: Three-way attention and relational knowledge for commonsense machine comprehension , Authors: Liang Wang , 2018\n5.  Title: Reinforced ranker-reader for open-domain question answering , Authors: Shuohang Wang Mo Yu... , 2018\n", "gt": "There has recently been growing interest in building better retrievers for open-domain QA. Wang et al. (2018) proposed a Reinforced Ranker-Reader model that ranks retrieved evidence and assigns different weights to evidence prior to processing by the reader. Min et al. (2018) demonstrated that for several popular MRC datasets (e.g. SQuAD, TriviaQA) most questions can be answered using only a few sentences rather than the entire document. Motivated by this observation, they built a sentence selector to gather this potential evidence for use by the reader model. Nishida et al. (2018) developed a multi-task learning (MTL) method for a retriever and reader in order to obtain a strong retriever that considers certain passages including the answer text as positive samples during training. The proposed MTL framework is still limited to the scenario when it is feasible to discover whether the passages contain the answer span. Although these works have achieved progress on open-domain QA by improving the ranking or selection of given evidence, few have focused on the scenario where the model needs to start by searching for the evidence itself. Scientific Question Answering (SQA) is a representative open-domain task that requires capability in both retrieval and reading comprehension. In this paper, we study question answering on the AI2 Reasoning Challenge (ARC) scientific QA dataset (Clark et al., 2018). This dataset contains elementary-level multiple-choice scientific questions from standardized tests and a large corpus of relevant information gathered from search engines. The dataset is partitioned into \u201cChallenge\u201d and \u201cEasy\u201d sets.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 249 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper proposes a new model called ET-RR (Essential-Term-Aware Retriever-Reader) to address the difficulties in open-domain question answering (QA) tasks. The model incorporates commonsense knowledge or improves retrieval capability to better capture partially related evidence. The model is trained on the ARC dataset, where it achieves an accuracy of 36.61% on the test set, ranking first on the official leaderboard. The model is also adapted to two datasets, RACE-Open and MCScript-Open, where it outperforms baseline models by a large margin. The model consists of an essential term selector and an attention-enhanced reader. The essential term selector selects a subset of essential terms from the question, which are then concatenated with each answer choice to formulate a query. The retrieved evidence is then fed to the reader to predict the final answer. The reader model consists of an input layer, attention layer, sequence modeling layer, fusion layer, and an output layer. The attention and fusion layers help the model to obtain a refined representation of one text sequence based on the understanding of another. The choice-interaction module is added to handle the semantic relations and differences between answer choices. The essential term selector is trained on a public dataset from Khashabi et al. (2017) which contains 2,223 annotated questions, each accompanied by four answer choices. The model is trained using a binary cross-entropy loss and during evaluation, words with yi greater than 0.5 are considered as essential terms. The model is evaluated on the ARC, RACE-Open and MCScript-Open datasets, where it outperforms existing retrieval-reader methods on all datasets.", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "This paper has made two major contributions: (1) a new neural reader architecture for multi-choice QA; (2) it is the first to introduce essential term selection to open-domain QA, to the best of my knowledge. The above two proposed modules (reader and selector) are not very novel, but are still valid contributions to me. Experiments on the ARC dataset shows that (1) the proposed reader itself improves over the state-of-the-art on the leaderboard; (2) introducing the essential term selector further improves the above results by about 2%.\n\nAlthough the paper also provides additional experiments on other datasets, I feel that the contributions of the proposed methods are not sufficiently verified. I would suggest the authors consider the following further experiments that I believe could improve its ratings:\n\n(1) The proposed reader works very well on ARC. However, besides the BiDAF, there is no comparison between the proposed reader and previous models on datasets other than ARC. In order to know whether the result generalizes or not, I think the authors should conduct experiments on the regular RACE or other multi-choice QA datasets, to fully test the reader model.\n\n(2) It is not clear whether the essential term selector could help on datasets other than science questions. Again, the authors reported results on two other datasets. However, on neither of these datasets the ET-RR was compared with ET-RR (Concat). Therefore, I have concerns that the proposed framework may only be significant on the ARC dataset.\n\nMoreover, it will be interesting to see whether the essential term selector can be learned from distant supervision. For example, using REINFORCE to learn the selector model with the rewards from the end-task performance. The current framework heavily relies on the supervised training data from (Khashabi et al., 2017), which may limit its usage to other datasets.\n5: Marginally below acceptance threshold", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 306 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper proposes a new model called ET-RR for open-domain question answering (QA). The model consists of an essential term selector and an attention-enhanced reader. The essential term selector selects essential terms from the question and concatenates them with each answer choice to formulate a more efficient query for the retriever. The retrieved evidence is then fed to the reader to predict the final answer. The reader model has an input layer, attention layer, sequence modeling layer, fusion layer, and output layer. The attention and fusion layers help the model to obtain a refined representation of one text sequence based on the understanding of another. The model is evaluated on the ARC dataset, where it achieves an accuracy of 36.61%, ranking first on the official leaderboard. The authors also adapt two datasets to the open-domain setting, RACE-Open and MCScript-Open, where the model outperforms baseline models by a large margin. Ablation studies show that each of the model's components contributes to its accuracy.\n\nOverall, the paper presents a well-structured and well-explained model for open-domain QA. The use of essential terms to improve the retrieval process is a novel approach that could be useful in other QA tasks. The attention-enhanced reader is also a valuable addition to the model, as it helps to refine the representation of the text sequences and improve the accuracy of the predictions.\n\nOne suggestion for future work would be to evaluate the model on other open-domain QA datasets to see how well it performs compared to other models. Additionally, it would be interesting to see how the model performs when given more complex questions and answers.\n\nAnother question that arises is how the model handles cases where there is not enough evidence to support any of the answer choices. In such cases, the model would need to be able to identify when it is unable to answer the question and return a \"don't know\" response. It would be interesting to see how the model performs in such cases and how it could be improved to handle them better.\n\nIn conclusion, the ET-RR model presents a promising approach to open-domain QA, and the authors should be commended for their work in developing and evaluating the model.", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Deep neural networks have achieved outstanding performance in many real-world applications with the expense of huge computational resources. The DenseNet, one of the recently proposed neural network architecture, has achieved the state-of-the-art performance in many visual tasks. However, it has great redundancy due to the dense connections of the internal structure, which leads to high computational costs in training such dense networks. To address this issue, we design a reinforcement learning framework to search for efficient DenseNet architectures with layer-wise pruning (LWP) for different tasks, while retaining the original advantages of DenseNet, such as feature reuse, short paths, etc. In this framework, an agent evaluates the importance of each connection between any two block layers, and prunes the redundant connections. In addition, a novel reward-shaping trick is introduced to make DenseNet reach a better trade-off between accuracy and float point operations (FLOPs). Our experiments show that DenseNet with LWP is more compact and efficient than existing alternatives.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 157 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper proposes a method for compressing and accelerating DenseNet, a type of neural network architecture that has shown state-of-the-art results on several highly competitive datasets. The method uses reinforcement learning to prune weights and connections in the DenseNet while maintaining good accuracy on a validation dataset. The proposed method is efficient and effective, reducing the number of parameters and FLOPs while maintaining or slightly degrading the prediction performance. The method is evaluated on several datasets, including CIFAR-10, CIFAR-100, and ImageNet, and shows promising results.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Multi-scale dense networks for resource efficient image classification , Authors: Gao Huang Danlu Chen... , 2017\n2.  Title: Densely connected convolutional networks , Authors: Gao Huang Zhuang Liu... , 2017\n3.  Title: Condensenet: An efficient densenet using learned group , Authors: Gao Huang Shichen Liu... , 2018\n4.  Title: Blockdrop: Dynamic inference paths in residual networks. 2018 , Authors: Zuxuan Wu Tushar Nagarajan... , 2018\n", "gt": "Huang et al. (2018) proposed group convolution to remove connections between layers in DenseNet for which this feature reuse is superfluous; Huang et al. (2017a) also suggested progressively update prediction for every test sample to unevenly adapt the amount of computational resource at inference time. The most related work is BlockDrop (Wu et al., 2018), which used reinforcement learning to prune weight dynamically at inference time but can only be applied to ResNet or its variants. In contrast, our approach is based on DenseNet, aiming to find efficient network structure based the densely connected features of DenseNet.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 97 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper proposes a method for compressing and accelerating DenseNet, a type of neural network architecture that has been successful in image classification tasks but is difficult to make efficient or real-time inference on low-end devices due to its intensive computational costs. The method uses reinforcement learning to train an agent to prune as many weights and connections as possible while maintaining good accuracy on a validation dataset. The agent learns to output a sequence of actions and receives reward according to the generated network structure on the validation datasets. The method is tested on several highly competitive datasets and is shown to reduce the number of parameters and flops while maintaining or slightly degrading the prediction performance.", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "The paper introduces RL based approach to prune layers in a DenseNet. This work extends BlockDrop to DenseNet architecture making the controller independent form the input image. The approach is evaluated on CIFAR10 and CIFAR100 datasets as well as on ImageNet showing promising results.\n\nIn order to improve the paper, the authors could take into consideration the following points:\n1. Given the similarity of the approach with BlockDrop, I would suggest to discuss it in the introduction section clearly stating the similarities and the differences with the proposed approach. \n2. BlockDrop seems to introduce a general framework of policy network to prune neural networks. However, the authors claim that BlockDrop \"can only be applied to ResNets or its variants\". Could the authors comment on this? \n3. In the abstract, the authors claim: \"Our experiments show that DenseNet with LWP is more compact and efficient than existing alternatives\". It is hard to asses if the statement is correct given the evidence presented in the experimental section. It is not clear if the method is more efficient and compact than others, e. g.  CondenseNet. \n4. In the experimental section, addressing the following questions would make the section stronger: What is more important FLOPs or number of parameters? What is the accuracy drop we should allow to pay for reduction in number of parameters or FLOPs?\n5. For the evaluation, I would suggest to show that the learned policy is better than a random one: e. g. not using the controller to define policy (in line 20 of the algorithm) and using a random random policy instead.\n6. In Table 1, some entries for DenseNet LWP are missing. Is the network converging for this setups? \n7. \\sigma is not explained in section 3.3. What is the intuition behind this hyper parameter?\n\n\nAdditional comments:\n1. It might be interesting to compare the method introduced in the paper to a scenario where the controller is conditioned on an input image and adaptively selects the connections/layers in DenseNet at inference time.\n2. It might be interesting to report the number of connections in Table 1 for all the models.\n\n4: Ok but not good enough - rejection", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 361 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper proposes a method for compressing and accelerating DenseNet, a type of neural network architecture that has shown state-of-the-art results on several highly competitive datasets. The method uses reinforcement learning to prune weights and connections in the DenseNet while maintaining good accuracy on a validation dataset. The proposed method is evaluated on several datasets and is shown to reduce the number of parameters and FLOPs while maintaining or slightly degrading the prediction performance. The method is also shown to outperform other approaches that require expert knowledge on network design.\n\nThe paper is well-written and the method is clearly described. The experimental results are thorough and provide evidence for the effectiveness of the proposed method. However, there are a few questions that arise from reading the paper. First, it is not clear how the reinforcement learning agent is trained. Is it trained using supervised learning or is there a reward function that guides the agent's learning? Second, it would be interesting to see a comparison of the proposed method with other methods on more datasets. Finally, it would be useful to have more details on the implementation of the method, such as the specific hyperparameters used and the hardware used for the experiments.\n\nOverall, the paper makes a valuable contribution to the field of deep learning and provides a promising approach for compressing and accelerating DenseNet.", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "The non-convex nature of the loss landscape of deep neural networks (DNN) lends them the intuition that over the course of training, stochastic optimization algorithms explore different regions of the loss surface by entering and escaping many local minima due to the noise induced by mini-batches. But is this really the case? This question couples the geometry of the DNN loss landscape with how stochastic optimization algorithms like SGD interact with it during training. Answering this question may help us qualitatively understand the dynamics of deep neural network optimization. We show evidence through qualitative and quantitative experiments that mini-batch SGD rarely crosses barriers during DNN optimization. As we show, the mini-batch induced noise helps SGD explore different regions of the loss surface using a seemingly different mechanism. To complement this finding, we also investigate the qualitative reason behind the slowing down of this exploration when using larger batch-sizes. We show this happens because gradients from larger batch-sizes align more with the top eigenvectors of the Hessian, which makes SGD oscillate in the proximity of the parameter initialization, thus preventing exploration.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 180 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe non-convexity of deep neural network (DNN) loss surfaces makes the behavior of optimization algorithms less intuitive compared to the convex setting. The paper studies stochastic gradient descent (SGD) as a diffusion process and shows that SGD rarely crosses barriers along its path during the course of training. The paper also shows that larger batch-sizes slow down the diffusion process and the reason behind this slow down is an oscillation behavior of SGD which prevents it from moving far away from initialization. The paper conducts experiments on multiple data sets, architectures and hyper-parameter settings and the findings hold true on all of them. The paper also introduces a qualitative view of how SGD explores different regions of the non-convex loss landscape of deep neural networks through empirical evidence.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Qualitatively characterizing neural network optimization problems , Authors: Ian J Goodfellow Oriol Vinyals... , 2014\n2.  Title: Train longer, generalize better: closing the generalization gap in large batch training of neural networks , Authors: Elad Hoffer Itay Hubara... , 2017\n3.  Title: On large-batch training for deep learning: Generalization gap and sharp minima , Authors: Nitish Shirish Keskar Dheevatsa Mudigere... , 2016\n4.  Title: Batch size matters: A diffusion approximation framework on nonconvex stochastic gradient descent , Authors: Chris Junchi Li Lei Li... , 2017\n5.  Title: Visualizing the loss landscape of neural nets , Authors: Hao Li Zheng Xu... , 2017\n6.  Title: Stochastic gradient descent as approximate bayesian inference , Authors: Stephan Mandt Matthew D Hoffman... , 2017\n", "gt": "There have been previous work on visualizing the loss surface although from different motivations. Perhaps Goodfellow et al. (2014) is most similar to our work since we use the loss interpolation tool suggested in their paper to perform our analysis. They perform interpolation between the initial and final parameters and based on their finding, draw the conclusion that the loss along the line connecting these two points does not have any barriers. We note that we use their tool for a different purpose and our conclusions are fundamentally different from theirs because we use the observations to investigate whether SGD crosses barriers during optimization over deep networks\u2019 loss landscape. Li et al. (2017b) also visualize the loss landscape of different network architectures. Our work is closely related to a number of recent papers that study SGD as a diffusion process because we present a complementary qualitative view to an aspect of their theory. Hoffer et al. (2017) hypothesize this view based on the evidence that the parameter distance moved by SGD from initialization as a function of the number of iterations resembles a diffusion process. Li et al. (2017a) hypothesize this behavior of SGD and theoretically show that this diffusion process would allow SGD to escape sharp local minima. The authors use this theoretical result to support the findings of Keskar et al. (2016) who find that SGD with small mini-batch size find wider minima. Kushner & Yin (2003); Mandt et al.", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 242 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper \"Exploring the Non-Convex Loss Landscape of Deep Neural Networks by Stochastic Gradient Descent\" by the authors Keskar, Jastrzebski, Chaudhari and Soatto presents a study on the behavior of stochastic gradient descent (SGD) during the optimization of deep neural networks. The authors argue that the non-convexity of the DNN loss surface makes the optimization process less intuitive and that the goal of optimization is not to find any minimum but rather ones that generalize well. They use the diffusion perspective of SGD to explore different regions of the non-convex loss landscape and show that SGD rarely crosses barriers along its path during the course of training. The authors also show that larger batch-sizes slow down the diffusion process and that this slow down is a result of the mini-batch gradients becoming increasingly aligned with the top eigenvectors of the Hessian. The findings of the paper hold for multiple datasets, architectures and hyper-parameter settings.\n\nThe paper \"Qualitatively characterizing neural network optimization problems\" by Goodfellow et al. presents a study on the optimization of neural networks and the behavior of the optimization algorithms. The authors use the concept of the loss landscape to visualize the optimization process and show that the optimization process is affected by the initialization, the architecture and the optimization algorithm. They also show that the optimization process is affected by the data and that the optimization algorithms have to explore different regions of the loss landscape to find a good minimum.\n\nThe paper \"Train longer, generalize better: closing the generalization gap in large batch training of neural networks\" by Hoffer et al. presents a study on the behavior of large batch training of neural networks. The authors argue that large batch training makes the optimization process slower and that this slow down is a result of the mini-batch gradients becoming increasingly aligned with the top eigenvectors of the Hessian. They also show that the generalization gap between the training and the test set is smaller for large batch sizes.\n\nThe paper \"Batch size matters: A diffusion approximation framework on nonconvex stochastic gradient descent\" by Li et al. presents a study on the behavior of stochastic gradient descent (SGD) during the optimization of deep neural networks. The authors argue that the batch size affects the diffusion process and that larger batch sizes lead to a slower", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "The subject of how a given algorithm explores the landscape is still a poorly understood area in training neural networks. There is a large body of recent work that attempts to shed light on this puzzle, and each one tries to claim their share in the furthering of the understanding of the relationship between the geometry of the landscape and the dynamics that one chooses in optimization. The present paper is a fine addition to the literature with interesting observations and novel questions, however, it falls short in many core areas: An apparent work in progress that has a great potential. \n\nA major question (that I hope will easily be addressed) is on the definition of the barrier itself. According to the text, a barrier is defined judging by the minima of two 1-dimensional segments that connect weights connecting three consecutive steps: if the minimum of the line segment defined by the latter step is larger than the former, then it declared that a barrier is crossed. In a low dimensional world, this makes total sense, however, I fail to understand what kind of barrier it implies on the geometry of the landscape: Can the 1-dimensional lines be on the sides of a valley? Can one find *another* 1-dimensional projection for which the inequality is broken? How do such dependencies change the understanding of the problem? And if one is indeed only interested in the flat line segments (since SGD is making discrete steps), then one can, in principle, observe barrier crossing in a convex problem, as well? Is there an argument for otherwise? Or if it is a notion that applies equally well in a convex case then how should we really think about the barrier crossing? On the opposite point of view, can one not imagine a barrier crossing that doesn't appear in this triangular inequality above?\n\nThe paper is full of empirical evidence that is guided by a simple observable that is very intuitive, however, it lacks a comprehensive discussion on the new quantity they propose that I consider a major flaw, but that I think (hope) that the authors can fix very easily. Some minor points that would improve the readability and clarity for the reader:\n- The figures are not very reader-friendly, this can be improved by better using the whitespaces in the paper but it can also be improved by finding further observables that would summarize the observations instead of showing individual consecutive line interpolations.\n- What are the values of the y-axis in Figure 5 and 6? Are they the top eigenvalues of the Hessian?\n- In the models that are compared in Figure 7, what are their generalization properties (early stopping and otherwise)?\n- The interpretation at the end of p. 6 may be a good motivation for the reader if it had been introduced earlier for that section.\n\n\n4: Ok but not good enough - rejection", "prompt": "A chat between a curious user and an artificial intelligence assistant.The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. Document is as follows. {} \nInstruction: {} The suggested output length is around 485 words.  \nASSISTANT: My english answer is:", "vicuna-13b-16k-flash_pred": "\n\nThe paper \"Stochastic gradient descent as a random walk on the loss landscape of deep neural networks\" by Y. Li, et al. presents a study on the behavior of stochastic gradient descent (SGD) during the optimization of deep neural networks. The authors use empirical evidence to complement the diffusion perspective of SGD with a qualitative view of how SGD explores different regions of the non-convex loss landscape. The paper presents a novel method of interpolating the loss surface between parameters before and after each training update to study the existence of barriers along the optimization path of SGD. The authors find that SGD rarely crosses barriers along its path during the course of training and instead moves over them. The paper also discusses the effect of batch size on exploration and shows that larger batch sizes slow down the diffusion process. The authors conduct experiments on multiple datasets, architectures, and hyperparameter settings and find that the conclusions hold true for all of them. Overall, the paper provides valuable insights into the behavior of SGD during the optimization of deep neural networks and has implications for the design of optimization algorithms for deep learning.\n\nMy japanese answer is:\n\n\u3053\u306e\u8ad6\u6587\u306f\u3001Y. Li\u3001et al.\u306b\u3088\u3063\u3066\u66f8\u304b\u308c\u305f\u300c\u6df1\u5c64\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u640d\u5931\u30e9\u30f3\u30c9\u30b9\u30b1\u30fc\u30d7\u4e0a\u3067\u306e\u96a8\u6a5fGradient Descent\u306e\u30e9\u30f3\u30c0\u30e0\u30a6\u30a9\u30fc\u30af\u300d\u3068\u3044\u3046\u30bf\u30a4\u30c8\u30eb\u3067\u3001Stochastic gradient descent\uff08SGD\uff09\u306e\u30c7\u30a3\u30fc\u30d7\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u6700\u9069\u5316\u4e2d\u306e\u884c\u52d5\u3092\u7814\u7a76\u3057\u3066\u3044\u307e\u3059\u3002\u8457\u8005\u305f\u3061\u306f\u3001\u7d4c\u9a13\u7684\u8a3c\u62e0\u3092\u7528\u3044\u3066\u3001SGD\u306e\u975e\u51f8\u640d\u5931\u30e9\u30f3\u30c9\u30b9\u30b1\u30fc\u30d7\u4e0a\u306e\u7570\u306a\u308b\u9818\u57df\u3092\u63a2\u7d22\u3059\u308b\u65b9\u6cd5\u3092\u88dc\u5b8c\u3057\u3001SGD\u306e\u62e1\u6563\u8996\u70b9\u3092\u8cea\u7684\u8996\u70b9\u3068\u3057\u3066\u88dc\u5b8c\u3057\u307e\u3059\u3002\u8457\u8005\u305f\u3061\u306f\u3001SGD\u306e\u6700\u9069\u5316\u30d1\u30b9\u306b\u6cbf\u3063\u3066\u306e\u969c", "evaluation": "rouge"}
